From 4a60b300f7959e1a386a107acbdeed5bfe9f2e03 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Tue, 2 Jun 2026 22:28:42 +0200
Subject: [PATCH 01/19] Refactor DrivePolicy architecture and configuration

- Updated DrivePolicy to use shared network architecture instead of split network.
- Changed input size parameters to specific sizes for ego, partner, lane, boundary, traffic control, and conditioning inputs.
- Modified encoder configuration to include activation functions and layer normalization options.
- Removed gigaflow architecture in favor of a more flexible encoder design.
- Adjusted observation size calculations to include counts of various features.
- Updated environment bindings and configuration files to reflect new parameter names and structures.
- Enhanced the DriveBackbone class to support new encoder configurations and pooling mechanisms.
- Updated the Drive class to accommodate changes in the backbone initialization and observation encoding.
---
 notebooks/01_observations.ipynb  |   4 +-
 notebooks/05_inference.ipynb     |  43 ++++---
 notebooks/06_architecture.ipynb  | 147 ++++++++++++----------
 notebooks/notebook_utils.py      |  15 ++-
 pufferlib/config/ocean/drive.ini |  23 ++--
 pufferlib/ocean/drive/drive.h    |  66 ++++------
 pufferlib/ocean/drive/drive.py   |   6 +-
 pufferlib/ocean/env_binding.h    |   1 +
 pufferlib/ocean/torch.py         | 202 ++++++++++++++++++++-----------
 9 files changed, 293 insertions(+), 214 deletions(-)

diff --git a/notebooks/01_observations.ipynb b/notebooks/01_observations.ipynb
index a72fcaa6f3..7931e59af9 100644
--- a/notebooks/01_observations.ipynb
+++ b/notebooks/01_observations.ipynb
@@ -165,6 +165,8 @@
     "idx += env.obs_slots_traffic_controls_n * env.traffic_control_features\n",
     "assert np.allclose(traffic_manual, traffic), \"traffic mismatch\"\n",
     "\n",
+    "idx += 4  # padding at end of obs\n",
+    "\n",
     "assert idx == obs.shape[1], f\"obs size mismatch: used {idx}, total {obs.shape[1]}\"\n",
     "print(f\"All slices match. Total features used: {idx}\")"
    ]
@@ -214,7 +216,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"heading_cos\", \"heading_sin\", \"speed\"]\n",
+    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"heading_cos\", \"heading_sin\", \"rel_vx\", \"rel_vy\"]\n",
     "active_mask = ~np.all(partners == 0, axis=1)\n",
     "n_active = active_mask.sum()\n",
     "print(f\"Active partners: {n_active}/{env.obs_slots_partners_n}\")\n",
diff --git a/notebooks/05_inference.ipynb b/notebooks/05_inference.ipynb
index c1f99ec009..9e2653d828 100644
--- a/notebooks/05_inference.ipynb
+++ b/notebooks/05_inference.ipynb
@@ -299,7 +299,7 @@
     "- **Ego**: speed, width, length, [jerk: steering, a_long, a_lat], lane_center_dist, lane_angle, speed_limit\n",
     "- **Conditioning** (if enabled): 17 reward coefs (goal_radius, goal_speed, collision, offroad, comfort, lane_align, vel_align, lane_center, center_bias, velocity, reverse, stop_line, timestep, overspeed, throttle, steer, acc) + target waypoints\n",
     "- **Target**: static=rel_x,rel_y,rel_z per waypoint; dynamic=rel_x,rel_y,rel_z,heading_cos,heading_sin per waypoint\n",
-    "- **Partners** (MAX_PARTNERS x 8): rel_x, rel_y, rel_z, length, width, heading_cos, heading_sin, speed\n",
+    "- **Partners** (MAX_PARTNERS x 9): rel_x, rel_y, rel_z, length, width, heading_cos, heading_sin, rel_vx, rel_vy\n",
     "- **Lanes** (MAX_LANES x 7): rel_x, rel_y, rel_z, seg_length, seg_width, dir_cos, dir_sin\n",
     "- **Boundaries** (MAX_BOUNDS x 7): same as lanes\n",
     "- **Traffic controls** (MAX_TRAFFIC x 7): rel_x1, rel_y1, rel_x2, rel_y2, rel_z, type, state"
@@ -390,9 +390,9 @@
     "# --- Partner summary ---\n",
     "n_visible = np.sum(np.any(partners != 0, axis=1))\n",
     "print(f\"\\n--- Partners: {n_visible}/{partners.shape[0]} visible ---\")\n",
-    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"width\", \"length\", \"heading_cos\", \"heading_sin\", \"speed\"]\n",
+    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"heading_cos\", \"heading_sin\", \"rel_vx\", \"rel_vy\"]\n",
     "for p in range(min(int(n_visible), 5)):\n",
-    "    vals = \", \".join(f\"{partner_labels[j]}={partners[p, j]:.3f}\" for j in range(8))\n",
+    "    vals = \", \".join(f\"{partner_labels[j]}={partners[p, j]:.3f}\" for j in range(env.partner_features))\n",
     "    print(f\"  [{p}] {vals}\")\n",
     "if n_visible > 5:\n",
     "    print(f\"  ... ({n_visible - 5} more)\")\n",
@@ -689,12 +689,14 @@
     "for i in range(partners.shape[0]):\n",
     "    if np.allclose(partners[i], 0):\n",
     "        continue\n",
-    "    rx, ry, rz, w, l, hc, hs, spd = partners[i]\n",
+    "    rx, ry, rz, length, width, hc, hs, rel_vx, rel_vy = partners[i]\n",
     "    heading = np.arctan2(hs, hc)\n",
-    "    rect = Rectangle((-l / 2, -w / 2), l, w, facecolor=\"orange\", edgecolor=\"black\", alpha=0.6, zorder=9)\n",
+    "    rect = Rectangle(\n",
+    "        (-length / 2, -width / 2), length, width, facecolor=\"orange\", edgecolor=\"black\", alpha=0.6, zorder=9\n",
+    "    )\n",
     "    rect.set_transform(plt.matplotlib.transforms.Affine2D().rotate(heading).translate(rx, ry) + ax.transData)\n",
     "    ax.add_patch(rect)\n",
-    "    ax.annotate(f\"{spd:.2f}\", (rx, ry), fontsize=7, ha=\"center\", color=\"darkred\", zorder=12)\n",
+    "    ax.annotate(f\"{rel_vx:.2f},{rel_vy:.2f}\", (rx, ry), fontsize=7, ha=\"center\", color=\"darkred\", zorder=12)\n",
     "part_mask = np.any(partners != 0, axis=1)\n",
     "if part_mask.any():\n",
     "    ax.scatter(\n",
@@ -847,7 +849,7 @@
    "outputs": [],
    "source": [
     "# Partner per-feature distributions (pooled over all agents + timesteps, visible only)\n",
-    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"width\", \"length\", \"heading_cos\", \"heading_sin\", \"speed\"]\n",
+    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"heading_cos\", \"heading_sin\", \"rel_vx\", \"rel_vy\"]\n",
     "obs_slots_partners_n = env.obs_slots_partners_n\n",
     "pf = env.partner_features\n",
     "\n",
@@ -861,17 +863,17 @@
     "\n",
     "all_partners = buf_stoch[\"obs\"][:, :, _p_start:_p_end].reshape(\n",
     "    -1, obs_slots_partners_n, pf\n",
-    ")  # (H*N, obs_slots_partners_n, 8)\n",
+    ")  # (H*N, obs_slots_partners_n, 9)\n",
     "# Mask: partner is visible if any feature != 0\n",
     "visible_mask = np.any(all_partners != 0, axis=2)  # (H*N, 16)\n",
-    "visible_partners = all_partners[visible_mask]  # (K, 8) — all visible partner observations\n",
+    "visible_partners = all_partners[visible_mask]  # (K, 9) — all visible partner observations\n",
     "\n",
     "print(\n",
     "    f\"Total partner obs: {all_partners.shape[0] * obs_slots_partners_n}, visible: {len(visible_partners)} \"\n",
     "    f\"({100 * len(visible_partners) / (all_partners.shape[0] * obs_slots_partners_n):.1f}%)\"\n",
     ")\n",
     "\n",
-    "fig, axes = plt.subplots(3, 3, figsize=(21, 10))\n",
+    "fig, axes = plt.subplots(2, 5, figsize=(21, 8))\n",
     "axes = axes.flatten()\n",
     "\n",
     "for i, label in enumerate(partner_labels):\n",
@@ -883,12 +885,13 @@
     "    axes[i].tick_params(labelsize=7)\n",
     "\n",
     "# rel_x vs rel_y scatter in last panel\n",
-    "axes[8].scatter(visible_partners[:, 0], visible_partners[:, 1], s=1, alpha=0.15, color=\"darkorange\")\n",
-    "axes[8].set_xlabel(\"rel_x\")\n",
-    "axes[8].set_ylabel(\"rel_y\")\n",
-    "axes[8].set_title(\"Partner positions (ego frame)\")\n",
-    "axes[8].set_aspect(\"equal\")\n",
-    "axes[8].grid(True, alpha=0.3)\n",
+    "pos_ax = axes[len(partner_labels)]\n",
+    "pos_ax.scatter(visible_partners[:, 0], visible_partners[:, 1], s=1, alpha=0.15, color=\"darkorange\")\n",
+    "pos_ax.set_xlabel(\"rel_x\")\n",
+    "pos_ax.set_ylabel(\"rel_y\")\n",
+    "pos_ax.set_title(\"Partner positions (ego frame)\")\n",
+    "pos_ax.set_aspect(\"equal\")\n",
+    "pos_ax.grid(True, alpha=0.3)\n",
     "\n",
     "fig.suptitle(\"Partner features: all visible, full rollout\", fontsize=13)\n",
     "plt.tight_layout()\n",
@@ -1464,7 +1467,7 @@
    "source": [
     "## Encoder analysis — what the policy encodes\n",
     "\n",
-    "Each obs layer has its own encoder projecting raw features → `input_size` embedding:\n",
+    "Each obs layer has its own encoder projecting raw features → embedding width:\n",
     "- **ego** and **conditioning** (reward coefs + target): single vector, no pooling.\n",
     "- **partners / lanes / boundaries / traffic**: per-slot encoder, padded slots masked to `-inf`, then **max-pooled** across slots → one embedding. Fully-padded layers are zeroed.\n",
     "\n",
@@ -1522,10 +1525,10 @@
     "for name, mod, rin, nslots, is_set in enc_inventory:\n",
     "    nparam = sum(p.numel() for p in mod.parameters())\n",
     "    print(\n",
-    "        f\"{name:>13s} | {rin:>6d} | {bb.input_size:>7d} | {nslots:>5d} | {('max' if is_set else '-'):>6s} | {nparam:>9,d}\"\n",
+    "        f\"{name:>13s} | {rin:>6d} | {mod[-1].out_features:>7d} | {nslots:>5d} | {('max' if is_set else '-'):>6s} | {nparam:>9,d}\"\n",
     "    )\n",
     "print(\n",
-    "    f\"\\nBackbone input = {len(enc_inventory)} x {bb.input_size} = {len(enc_inventory) * bb.input_size} -> backbone -> {bb.out_dim}\"\n",
+    "    f\"\\nBackbone input = {sum(mod[-1].out_features for _, mod, _, _, _ in enc_inventory)} -> backbone -> {bb.out_dim}\"\n",
     ")\n",
     "\n",
     "# Capture pre-pool encoder outputs via forward hooks\n",
@@ -1581,7 +1584,7 @@
     "    masked = captured[name].masked_fill(pad[name].unsqueeze(2), -torch.inf)\n",
     "    vm = (~pad[name]).any(dim=1)\n",
     "    valid_sample[name] = vm\n",
-    "    winners[name] = masked.max(dim=1).indices  # (B, input_size): winning slot per dim\n",
+    "    winners[name] = masked.max(dim=1).indices  # (B, embedding dim): winning slot per dim\n",
     "    pooled[name] = torch.where(vm.unsqueeze(1), masked.max(dim=1).values, torch.zeros_like(masked.max(dim=1).values))\n",
     "\n",
     "for name in (\"ego\", \"conditioning\"):\n",
diff --git a/notebooks/06_architecture.ipynb b/notebooks/06_architecture.ipynb
index 8df1731aba..260b636444 100644
--- a/notebooks/06_architecture.ipynb
+++ b/notebooks/06_architecture.ipynb
@@ -31,35 +31,44 @@
     "ACTOR_NUM_LAYERS = 3\n",
     "CRITIC_HIDDEN_SIZE = 64\n",
     "CRITIC_NUM_LAYERS = 2\n",
-    "SPLIT_NETWORK = False\n",
-    "ENCODER_GIGAFLOW = True\n",
-    "DROPOUT = 0.0\n",
+    "SHARED_NETWORK = True\n",
+    "ENCODER_ACTIVATION = \"tanh\"\n",
+    "ENCODER_LAYER_NORM = True\n",
+    "BACKBONE_ACTIVATION = \"gelu\"\n",
+    "BACKBONE_LAYER_NORM = False\n",
     "\n",
     "env, obs, info = make_drive_env()\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "policy = DrivePolicy(\n",
     "    env,\n",
-    "    input_size=INPUT_SIZE,\n",
+    "    ego_input_size=INPUT_SIZE,\n",
+    "    partner_input_size=INPUT_SIZE,\n",
+    "    lane_input_size=INPUT_SIZE,\n",
+    "    boundary_input_size=INPUT_SIZE,\n",
+    "    traffic_control_input_size=INPUT_SIZE,\n",
+    "    conditioning_input_size=INPUT_SIZE,\n",
     "    backbone_hidden_size=BACKBONE_HIDDEN_SIZE,\n",
     "    backbone_num_layers=BACKBONE_NUM_LAYERS,\n",
     "    actor_hidden_size=ACTOR_HIDDEN_SIZE,\n",
     "    actor_num_layers=ACTOR_NUM_LAYERS,\n",
     "    critic_hidden_size=CRITIC_HIDDEN_SIZE,\n",
     "    critic_num_layers=CRITIC_NUM_LAYERS,\n",
-    "    split_network=SPLIT_NETWORK,\n",
-    "    encoder_gigaflow=ENCODER_GIGAFLOW,\n",
-    "    dropout=DROPOUT,\n",
+    "    encoder_activation=ENCODER_ACTIVATION,\n",
+    "    encoder_layer_norm=ENCODER_LAYER_NORM,\n",
+    "    backbone_activation=BACKBONE_ACTIVATION,\n",
+    "    backbone_layer_norm=BACKBONE_LAYER_NORM,\n",
+    "    shared_network=SHARED_NETWORK,\n",
     ").to(device)\n",
     "\n",
     "print(f\"Device: {device}\")\n",
     "print(f\"Obs dim: {obs.shape[1]}\")\n",
     "print(f\"Action dim: {policy.atn_dim}\")\n",
-    "print(f\"Split network: {SPLIT_NETWORK}\")\n",
+    "print(f\"Shared network: {SHARED_NETWORK}\")\n",
     "print(f\"Backbone: {BACKBONE_HIDDEN_SIZE} x {BACKBONE_NUM_LAYERS}L\")\n",
     "print(f\"Actor: {ACTOR_HIDDEN_SIZE} x {ACTOR_NUM_LAYERS}L\")\n",
     "print(f\"Critic: {CRITIC_HIDDEN_SIZE} x {CRITIC_NUM_LAYERS}L\")\n",
-    "print(f\"Encoder gigaflow: {ENCODER_GIGAFLOW}, Dropout: {DROPOUT}\")"
+    "print(f\"Encoder: {ENCODER_ACTIVATION}, LayerNorm: {ENCODER_LAYER_NORM}\")"
    ]
   },
   {
@@ -95,20 +104,19 @@
     "backbone = policy.actor_backbone\n",
     "cond_dim = backbone.conditioning_dim\n",
     "\n",
-    "# Collect encoder info — encoder_gigaflow adds Tanh+Dropout between LN and second Linear\n",
-    "# ego, partner, conditioning use encoder_gigaflow; lane, boundary, traffic_ctrl use dropout\n",
+    "# Collect encoder info\n",
     "encoders = [\n",
-    "    (\"ego\", env.ego_features, 1, \"direct\", ENCODER_GIGAFLOW),\n",
-    "    (\"conditioning\", cond_dim, 1, \"direct\", ENCODER_GIGAFLOW) if cond_dim > 0 else None,\n",
-    "    (\"partner\", env.partner_features, env.obs_slots_partners_n, \"max-pool\", ENCODER_GIGAFLOW),\n",
-    "    (\"lane\", env.road_features, env.obs_slots_lane_kept, \"max-pool\", ENCODER_GIGAFLOW),\n",
-    "    (\"boundary\", env.road_features, env.obs_slots_boundary_kept, \"max-pool\", ENCODER_GIGAFLOW),\n",
+    "    (\"ego\", env.ego_features, 1, \"direct\", INPUT_SIZE),\n",
+    "    (\"conditioning\", cond_dim, 1, \"direct\", INPUT_SIZE) if cond_dim > 0 else None,\n",
+    "    (\"partner\", env.partner_features, env.obs_slots_partners_n, \"max-pool\", INPUT_SIZE),\n",
+    "    (\"lane\", env.road_features, env.obs_slots_lane_kept, \"max-pool\", INPUT_SIZE),\n",
+    "    (\"boundary\", env.road_features, env.obs_slots_boundary_kept, \"max-pool\", INPUT_SIZE),\n",
     "    (\n",
     "        \"traffic_ctrl\",\n",
     "        env.traffic_control_features - 2 + binding.NUM_TRAFFIC_CONTROL_TYPES + binding.NUM_TRAFFIC_CONTROL_STATES,\n",
     "        env.obs_slots_traffic_controls_n,\n",
     "        \"max-pool (onehot)\",\n",
-    "        ENCODER_GIGAFLOW,\n",
+    "        INPUT_SIZE,\n",
     "    ),\n",
     "]\n",
     "encoders = [e for e in encoders if e is not None]\n",
@@ -123,36 +131,25 @@
     "colors = plt.cm.Set2(np.linspace(0, 1, n_enc))\n",
     "\n",
     "# Draw encoders\n",
-    "for i, ((name, in_f, n_obj, agg, gigaflow), y, c) in enumerate(zip(encoders, y_positions, colors)):\n",
+    "for i, ((name, in_f, n_obj, agg, out_size), y, c) in enumerate(zip(encoders, y_positions, colors)):\n",
     "    # Input box\n",
     "    label = f\"{name}\\n{n_obj}x{in_f}\" if n_obj > 1 else f\"{name}\\n{in_f}\"\n",
     "    ax.add_patch(plt.Rectangle((0.2, y - 0.3), 1.6, 0.6, facecolor=c, edgecolor=\"black\", lw=1.2, alpha=0.8))\n",
     "    ax.text(1.0, y, label, ha=\"center\", va=\"center\", fontsize=8, fontweight=\"bold\")\n",
     "\n",
-    "    # Encoder box — show gigaflow arch vs standard\n",
+    "    # Encoder box\n",
     "    ax.add_patch(plt.Rectangle((2.5, y - 0.25), 2.0, 0.5, facecolor=\"lightyellow\", edgecolor=\"black\", lw=1))\n",
-    "    ax.text(3.5, y + 0.05, f\"Linear({in_f},{INPUT_SIZE})\", ha=\"center\", va=\"center\", fontsize=7)\n",
-    "    if gigaflow:\n",
-    "        ax.text(\n",
-    "            3.5,\n",
-    "            y - 0.12,\n",
-    "            f\"LN+Tanh+Drop+Linear({INPUT_SIZE},{INPUT_SIZE})\",\n",
-    "            ha=\"center\",\n",
-    "            va=\"center\",\n",
-    "            fontsize=5.5,\n",
-    "            color=\"darkgreen\",\n",
-    "        )\n",
-    "    else:\n",
-    "        drop_str = f\"+Drop({DROPOUT})\" if DROPOUT > 0 and name not in (\"ego\", \"partner\", \"conditioning\") else \"\"\n",
-    "        ax.text(\n",
-    "            3.5,\n",
-    "            y - 0.12,\n",
-    "            f\"LN{drop_str}+Linear({INPUT_SIZE},{INPUT_SIZE})\",\n",
-    "            ha=\"center\",\n",
-    "            va=\"center\",\n",
-    "            fontsize=6,\n",
-    "            color=\"gray\",\n",
-    "        )\n",
+    "    ax.text(3.5, y + 0.05, f\"Linear({in_f},{out_size})\", ha=\"center\", va=\"center\", fontsize=7)\n",
+    "    ln_label = \"LN+\" if ENCODER_LAYER_NORM else \"\"\n",
+    "    ax.text(\n",
+    "        3.5,\n",
+    "        y - 0.12,\n",
+    "        f\"{ln_label}{ENCODER_ACTIVATION}+Linear({out_size},{out_size})\",\n",
+    "        ha=\"center\",\n",
+    "        va=\"center\",\n",
+    "        fontsize=6,\n",
+    "        color=\"gray\",\n",
+    "    )\n",
     "\n",
     "    # Aggregation\n",
     "    if n_obj > 1:\n",
@@ -194,14 +191,13 @@
     "ax.annotate(\"\", xy=(9.0, 6.0), xytext=(8.8, 5.3), arrowprops=dict(arrowstyle=\"->\", lw=1.2))\n",
     "ax.annotate(\"\", xy=(9.0, 4.0), xytext=(8.8, 4.7), arrowprops=dict(arrowstyle=\"->\", lw=1.2))\n",
     "\n",
-    "split_label = \"SPLIT\" if SPLIT_NETWORK else \"SHARED\"\n",
+    "split_label = \"SHARED\" if SHARED_NETWORK else \"SPLIT\"\n",
     "ax.text(8.9, 4.55, split_label, ha=\"center\", va=\"center\", fontsize=7, color=\"red\", fontweight=\"bold\")\n",
     "\n",
-    "gigaflow_label = \"GIGAFLOW\" if ENCODER_GIGAFLOW else \"STANDARD\"\n",
     "ax.text(\n",
     "    5.0,\n",
     "    0.3,\n",
-    "    f\"Encoder mode: {gigaflow_label} | Dropout: {DROPOUT}\",\n",
+    "    f\"Encoder: {ENCODER_ACTIVATION} | LayerNorm: {ENCODER_LAYER_NORM}\",\n",
     "    ha=\"center\",\n",
     "    va=\"center\",\n",
     "    fontsize=8,\n",
@@ -210,7 +206,7 @@
     ")\n",
     "\n",
     "ax.set_title(\n",
-    "    f\"DrivePolicy Architecture (input_size={INPUT_SIZE}, backbone={BACKBONE_HIDDEN_SIZE})\",\n",
+    "    f\"DrivePolicy Architecture (encoder_size={INPUT_SIZE}, backbone={BACKBONE_HIDDEN_SIZE})\",\n",
     "    fontsize=12,\n",
     "    fontweight=\"bold\",\n",
     ")\n",
@@ -258,7 +254,7 @@
     "    print(f\"{n:>25s} | {c:>10,d} | {c / total:>5.1%}\")\n",
     "print(\"-\" * 48)\n",
     "print(f\"{'TOTAL':>25s} | {total:>10,d}\")\n",
-    "if SPLIT_NETWORK:\n",
+    "if not SHARED_NETWORK:\n",
     "    critic_bb = count_params(policy.critic_backbone)\n",
     "    print(f\"{'+ critic_backbone':>25s} | {critic_bb:>10,d}\")\n",
     "    print(f\"{'GRAND TOTAL':>25s} | {total + critic_bb:>10,d}\")\n",
@@ -545,12 +541,12 @@
    "outputs": [],
    "source": [
     "configs = [\n",
-    "    {\"name\": \"tiny\", \"input_size\": 32, \"backbone_hidden_size\": 64},\n",
-    "    {\"name\": \"small\", \"input_size\": 64, \"backbone_hidden_size\": 128},\n",
-    "    {\"name\": \"medium\", \"input_size\": 128, \"backbone_hidden_size\": 256, \"backbone_num_layers\": 2},\n",
+    "    {\"name\": \"tiny\", \"encoder_size\": 32, \"backbone_hidden_size\": 64},\n",
+    "    {\"name\": \"small\", \"encoder_size\": 64, \"backbone_hidden_size\": 128},\n",
+    "    {\"name\": \"medium\", \"encoder_size\": 128, \"backbone_hidden_size\": 256, \"backbone_num_layers\": 2},\n",
     "    {\n",
     "        \"name\": \"large\",\n",
-    "        \"input_size\": 128,\n",
+    "        \"encoder_size\": 128,\n",
     "        \"backbone_hidden_size\": 512,\n",
     "        \"backbone_num_layers\": 2,\n",
     "        \"actor_num_layers\": 2,\n",
@@ -560,7 +556,7 @@
     "    },\n",
     "    {\n",
     "        \"name\": \"xlarge\",\n",
-    "        \"input_size\": 256,\n",
+    "        \"encoder_size\": 256,\n",
     "        \"backbone_hidden_size\": 1024,\n",
     "        \"backbone_num_layers\": 3,\n",
     "        \"actor_num_layers\": 2,\n",
@@ -568,32 +564,50 @@
     "        \"critic_num_layers\": 2,\n",
     "        \"critic_hidden_size\": 512,\n",
     "    },\n",
-    "    {\"name\": \"small+giga\", \"input_size\": 64, \"backbone_hidden_size\": 128, \"encoder_gigaflow\": True, \"dropout\": 0.1},\n",
+    "    {\"name\": \"small+tanh\", \"encoder_size\": 64, \"backbone_hidden_size\": 128, \"encoder_activation\": \"tanh\"},\n",
     "    {\n",
-    "        \"name\": \"medium+giga\",\n",
-    "        \"input_size\": 128,\n",
+    "        \"name\": \"medium+tanh\",\n",
+    "        \"encoder_size\": 128,\n",
     "        \"backbone_hidden_size\": 256,\n",
     "        \"backbone_num_layers\": 2,\n",
-    "        \"encoder_gigaflow\": True,\n",
-    "        \"dropout\": 0.1,\n",
+    "        \"encoder_activation\": \"tanh\",\n",
     "    },\n",
     "]\n",
     "\n",
     "POLICY_DEFAULTS = {\n",
+    "    \"ego_input_size\": 64,\n",
+    "    \"partner_input_size\": 64,\n",
+    "    \"lane_input_size\": 64,\n",
+    "    \"boundary_input_size\": 64,\n",
+    "    \"traffic_control_input_size\": 64,\n",
+    "    \"conditioning_input_size\": 64,\n",
     "    \"backbone_num_layers\": 1,\n",
     "    \"actor_hidden_size\": 128,\n",
     "    \"actor_num_layers\": 0,\n",
     "    \"critic_hidden_size\": 128,\n",
     "    \"critic_num_layers\": 0,\n",
-    "    \"encoder_gigaflow\": False,\n",
-    "    \"dropout\": 0.0,\n",
-    "    \"split_network\": False,\n",
+    "    \"encoder_activation\": \"relu\",\n",
+    "    \"encoder_layer_norm\": True,\n",
+    "    \"backbone_activation\": \"gelu\",\n",
+    "    \"backbone_layer_norm\": False,\n",
+    "    \"shared_network\": True,\n",
     "}\n",
     "\n",
     "results = []\n",
     "for cfg in configs:\n",
-    "    name = cfg.pop(\"name\")\n",
-    "    full_cfg = {**POLICY_DEFAULTS, **cfg}\n",
+    "    name = cfg[\"name\"]\n",
+    "    encoder_size = cfg.get(\"encoder_size\", POLICY_DEFAULTS[\"ego_input_size\"])\n",
+    "    full_cfg = {**POLICY_DEFAULTS, **{k: v for k, v in cfg.items() if k not in (\"name\", \"encoder_size\")}}\n",
+    "    full_cfg.update(\n",
+    "        {\n",
+    "            \"ego_input_size\": encoder_size,\n",
+    "            \"partner_input_size\": encoder_size,\n",
+    "            \"lane_input_size\": encoder_size,\n",
+    "            \"boundary_input_size\": encoder_size,\n",
+    "            \"traffic_control_input_size\": encoder_size,\n",
+    "            \"conditioning_input_size\": encoder_size,\n",
+    "        }\n",
+    "    )\n",
     "    p = DrivePolicy(env, **full_cfg).to(device)\n",
     "    n_params = sum(pp.numel() for pp in p.parameters())\n",
     "\n",
@@ -607,17 +621,16 @@
     "            torch.cuda.synchronize()\n",
     "        ms_per_fwd = (time.time() - t0) / 100 * 1000\n",
     "\n",
-    "    results.append({\"name\": name, \"params\": n_params, \"ms/fwd\": ms_per_fwd, **cfg})\n",
-    "    cfg[\"name\"] = name  # restore\n",
+    "    results.append({\"name\": name, \"encoder_size\": encoder_size, \"params\": n_params, \"ms/fwd\": ms_per_fwd, **full_cfg})\n",
     "    del p\n",
     "\n",
     "print(\n",
-    "    f\"{'Config':>12s} | {'input':>5s} | {'bb_h':>5s} | {'bb_L':>4s} | {'act_h':>5s} | {'act_L':>5s} | {'crt_h':>5s} | {'crt_L':>5s} | {'giga':>5s} | {'Params':>10s} | {'ms/fwd':>8s}\"\n",
+    "    f\"{'Config':>12s} | {'enc':>5s} | {'bb_h':>5s} | {'bb_L':>4s} | {'act_h':>5s} | {'act_L':>5s} | {'crt_h':>5s} | {'crt_L':>5s} | {'enc_act':>7s} | {'Params':>10s} | {'ms/fwd':>8s}\"\n",
     ")\n",
     "print(\"-\" * 105)\n",
     "for r in results:\n",
     "    print(\n",
-    "        f\"{r['name']:>12s} | {r['input_size']:>5d} | {r.get('backbone_hidden_size', 1024):>5d} | {r.get('backbone_num_layers', 1):>4d} | {r.get('actor_hidden_size', 1024):>5d} | {r.get('actor_num_layers', 1):>5d} | {r.get('critic_hidden_size', 1024):>5d} | {r.get('critic_num_layers', 1):>5d} | {str(r.get('encoder_gigaflow', False)):>5s} | {r['params']:>10,d} | {r['ms/fwd']:>7.2f}ms\"\n",
+    "        f\"{r['name']:>12s} | {r['encoder_size']:>5d} | {r['backbone_hidden_size']:>5d} | {r['backbone_num_layers']:>4d} | {r['actor_hidden_size']:>5d} | {r['actor_num_layers']:>5d} | {r['critic_hidden_size']:>5d} | {r['critic_num_layers']:>5d} | {r['encoder_activation']:>7s} | {r['params']:>10,d} | {r['ms/fwd']:>7.2f}ms\"\n",
     "    )\n",
     "\n",
     "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
@@ -625,11 +638,11 @@
     "params = [r[\"params\"] for r in results]\n",
     "times = [r[\"ms/fwd\"] for r in results]\n",
     "\n",
-    "bar_colors = [\"coral\" if r.get(\"encoder_gigaflow\") else \"steelblue\" for r in results]\n",
+    "bar_colors = [\"coral\" if r[\"encoder_activation\"] == \"tanh\" else \"steelblue\" for r in results]\n",
     "\n",
     "axes[0].bar(names, params, color=bar_colors, edgecolor=\"black\")\n",
     "axes[0].set_ylabel(\"Parameters\")\n",
-    "axes[0].set_title(\"Parameter Count (orange=gigaflow)\")\n",
+    "axes[0].set_title(\"Parameter Count (orange=tanh encoder)\")\n",
     "axes[0].tick_params(axis=\"x\", rotation=30)\n",
     "for i, v in enumerate(params):\n",
     "    axes[0].text(i, v, f\"{v:,}\", ha=\"center\", va=\"bottom\", fontsize=7)\n",
diff --git a/notebooks/notebook_utils.py b/notebooks/notebook_utils.py
index 02164349f7..100d15c011 100644
--- a/notebooks/notebook_utils.py
+++ b/notebooks/notebook_utils.py
@@ -85,16 +85,23 @@
 }
 
 DEFAULT_POLICY_KWARGS = {
-    "input_size": 64,
+    "ego_input_size": 64,
+    "partner_input_size": 64,
+    "lane_input_size": 64,
+    "boundary_input_size": 64,
+    "traffic_control_input_size": 64,
+    "conditioning_input_size": 64,
     "backbone_hidden_size": 128,
     "backbone_num_layers": 1,
     "actor_hidden_size": 128,
     "actor_num_layers": 0,
     "critic_hidden_size": 128,
     "critic_num_layers": 0,
-    "encoder_gigaflow": True,
-    "dropout": 0.0,
-    "split_network": False,
+    "encoder_activation": "tanh",
+    "encoder_layer_norm": True,
+    "backbone_activation": "gelu",
+    "backbone_layer_norm": False,
+    "shared_network": True,
 }
 
 
diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini
index 813d1970eb..63d87ac303 100644
--- a/pufferlib/config/ocean/drive.ini
+++ b/pufferlib/config/ocean/drive.ini
@@ -11,13 +11,23 @@ num_workers = auto
 batch_size = auto
 
 [policy]
-; Encoder layer
-input_size = 64
-encoder_gigaflow = True
-dropout = 0.0
+; Encoder layer (per-encoder embedding width)
+ego_input_size = 64
+partner_input_size = 64
+lane_input_size = 64
+boundary_input_size = 64
+traffic_control_input_size = 64
+conditioning_input_size = 64
+; Encoder activation - options: "relu", "tanh", "gelu"
+encoder_activation = "relu"
+encoder_layer_norm = True
+mask_padded_observations = False
 ; Shared backbone layer
 backbone_hidden_size = 512
 backbone_num_layers = 4
+; Backbone activation - options: "relu", "tanh", "gelu"
+backbone_activation = "gelu"
+backbone_layer_norm = False
 ; Actor head layer
 actor_hidden_size = 512
 actor_num_layers = 0
@@ -25,7 +35,7 @@ actor_num_layers = 0
 critic_hidden_size = 512
 critic_num_layers = 0
 ; Dual or shared actor-critic backbone
-split_network = False
+shared_network = True
 
 [rnn]
 input_size = 512
@@ -98,7 +108,7 @@ reward_lane_align = 0.025
 reward_vel_align = 1.0
 reward_lane_center = 0.0038
 reward_center_bias = 0.0
-reward_velocity = 0.1
+reward_velocity = 0.0025
 reward_reverse = 0.005
 reward_timestep = 0.000025
 reward_overspeed = 0.05
@@ -170,7 +180,6 @@ adam_beta2 = 0.999
 adam_eps = 1e-8
 vtrace_c_clip = 1
 vtrace_rho_clip = 1
-ppo_granularity = auto
 adv_sampling_prio_alpha = 0.8499999999999999
 adv_sampling_prio_beta0 = 0.8499999999999999
 adv_filter_ewma_beta = 0.25
diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h
index 1dc87e6fd3..2397310f36 100644
--- a/pufferlib/ocean/drive/drive.h
+++ b/pufferlib/ocean/drive/drive.h
@@ -119,9 +119,9 @@
 // Observation feature counts
 #define EGO_FEATURES 10
 #define ROAD_FEATURES 7
-#define PARTNER_FEATURES 8
+#define PARTNER_FEATURES 9
 #define TRAFFIC_CONTROL_FEATURES 7
-#define PADDED_OBSERVATION_VALUE -0.001f
+#define OBS_COUNT_FEATURES 4
 #define STATIC_TARGET_FEATURES 3
 #define DYNAMIC_TARGET_FEATURES 5
 
@@ -3700,29 +3700,8 @@ static int compute_observation_size(Drive *env) {
                                                               : 0;
     return EGO_FEATURES + PARTNER_FEATURES * env->obs_slots_partners_n
         + ROAD_FEATURES * (env->obs_slots_lane_kept + env->obs_slots_boundary_kept)
-        + TRAFFIC_CONTROL_FEATURES * env->obs_slots_traffic_controls_n + env->reward_conditioning * NUM_REWARD_COEFS
-        + env->num_target_waypoints * target_features;
-}
-
-// Fill `rows` x `features` observation slots with the padding sentinel.
-static inline void fill_padded_observation_rows(float *obs, int rows, int features) {
-    for (int r = 0; r < rows; r++) {
-        for (int c = 0; c < features; c++) {
-            obs[r * features + c] = PADDED_OBSERVATION_VALUE;
-        }
-    }
-}
-
-// Pad `rows` traffic-control slots with the sentinel; type/state columns set to NONE/UNKNOWN.
-static inline void fill_padded_traffic_control_rows(float *obs, int rows) {
-    for (int r = 0; r < rows; r++) {
-        int base = r * TRAFFIC_CONTROL_FEATURES;
-        for (int c = 0; c < TRAFFIC_CONTROL_FEATURES - 2; c++) {
-            obs[base + c] = PADDED_OBSERVATION_VALUE;
-        }
-        obs[base + TRAFFIC_CONTROL_FEATURES - 2] = TRAFFIC_CONTROL_TYPE_NONE;
-        obs[base + TRAFFIC_CONTROL_FEATURES - 1] = TRAFFIC_CONTROL_STATE_UNKNOWN;
-    }
+        + TRAFFIC_CONTROL_FEATURES * env->obs_slots_traffic_controls_n + OBS_COUNT_FEATURES
+        + env->reward_conditioning * NUM_REWARD_COEFS + env->num_target_waypoints * target_features;
 }
 
 void allocate(Drive *env) {
@@ -4473,7 +4452,7 @@ static int write_reward_target_obs(Drive *env, Agent *ego, float *obs, int obs_i
 static int write_partner_obs(Drive *env, Agent *ego, int agent_idx, float *obs, int obs_idx, int *partner_count) {
     if (ego->is_blind_partner && random_uniform(0.0f, 1.0f) < env->partner_blindness_trigger_prob) {
         int partner_obs_stride = env->obs_slots_partners_n * PARTNER_FEATURES;
-        fill_padded_observation_rows(&obs[obs_idx], env->obs_slots_partners_n, PARTNER_FEATURES);
+        memset(&obs[obs_idx], 0, partner_obs_stride * sizeof(float));
         *partner_count = 0;
         return obs_idx + partner_obs_stride;
     }
@@ -4534,7 +4513,7 @@ static int write_partner_obs(Drive *env, Agent *ego, int agent_idx, float *obs,
         float rel_x, rel_y, rel_heading_x, rel_heading_y, rel_vx, rel_vy;
         project_vector_to_ego_frame(ego, nearby_agents[j].dx, nearby_agents[j].dy, &rel_x, &rel_y);
         project_vector_to_ego_frame(ego, other->cos_heading, other->sin_heading, &rel_heading_x, &rel_heading_y);
-        project_vector_to_ego_frame(ego, other->sim_vx, other->sim_vy, &rel_vx, &rel_vy);
+        project_vector_to_ego_frame(ego, other->sim_vx - ego->sim_vx, other->sim_vy - ego->sim_vy, &rel_vx, &rel_vy);
         obs[obs_idx++] = rel_x / env->obs_norm_xy_offset_m;
         obs[obs_idx++] = rel_y / env->obs_norm_xy_offset_m;
         obs[obs_idx++] = nearby_agents[j].dz / Z_BUFFER;
@@ -4542,12 +4521,12 @@ static int write_partner_obs(Drive *env, Agent *ego, int agent_idx, float *obs,
         obs[obs_idx++] = other->sim_width / env->obs_norm_veh_width_m;
         obs[obs_idx++] = rel_heading_x;
         obs[obs_idx++] = rel_heading_y;
-        obs[obs_idx++] = other->sim_speed_signed / MAX_SPEED;
+        obs[obs_idx++] = rel_vx / (2.0f * MAX_SPEED);
+        obs[obs_idx++] = rel_vy / (2.0f * MAX_SPEED);
         partners_written++;
     }
 
     *partner_count = partners_written;
-    fill_padded_observation_rows(&obs[obs_idx], env->obs_slots_partners_n - partners_written, PARTNER_FEATURES);
     return obs_idx + (env->obs_slots_partners_n - partners_written) * PARTNER_FEATURES;
 }
 
@@ -4646,28 +4625,28 @@ static int write_road_obs(Drive *env, Agent *ego, float *obs, int obs_idx, int *
         subsample_road_observation_rows(lanes_buffer, lanes_found, lanes_to_copy);
         subsample_road_observation_rows(boundaries_buffer, boundaries_found, boundaries_to_copy);
         memcpy(&obs[lane_obs_idx], lanes_buffer, lanes_to_copy * ROAD_FEATURES * sizeof(float));
-        fill_padded_observation_rows(
+        memset(
             &obs[lane_obs_idx + lanes_to_copy * ROAD_FEATURES],
-            env->obs_slots_lane_kept - lanes_to_copy,
-            ROAD_FEATURES);
+            0,
+            (env->obs_slots_lane_kept - lanes_to_copy) * ROAD_FEATURES * sizeof(float));
         memcpy(&obs[boundary_obs_idx], boundaries_buffer, boundaries_to_copy * ROAD_FEATURES * sizeof(float));
-        fill_padded_observation_rows(
+        memset(
             &obs[boundary_obs_idx + boundaries_to_copy * ROAD_FEATURES],
-            env->obs_slots_boundary_kept - boundaries_to_copy,
-            ROAD_FEATURES);
+            0,
+            (env->obs_slots_boundary_kept - boundaries_to_copy) * ROAD_FEATURES * sizeof(float));
         return obs_idx;
     }
 
     *lane_count = lanes_found;
     *boundary_count = boundaries_found;
-    fill_padded_observation_rows(
+    memset(
         &obs[lane_obs_idx + lanes_found * ROAD_FEATURES],
-        env->obs_slots_lane_kept - lanes_found,
-        ROAD_FEATURES);
-    fill_padded_observation_rows(
+        0,
+        (env->obs_slots_lane_kept - lanes_found) * ROAD_FEATURES * sizeof(float));
+    memset(
         &obs[boundary_obs_idx + boundaries_found * ROAD_FEATURES],
-        env->obs_slots_boundary_kept - boundaries_found,
-        ROAD_FEATURES);
+        0,
+        (env->obs_slots_boundary_kept - boundaries_found) * ROAD_FEATURES * sizeof(float));
     return obs_idx;
 }
 
@@ -4740,7 +4719,6 @@ static int write_traffic_control_obs(Drive *env, Agent *ego, float *obs, int obs
     }
 
     *traffic_control_count = controls_written;
-    fill_padded_traffic_control_rows(&obs[obs_idx], env->obs_slots_traffic_controls_n - controls_written);
     return obs_idx + (env->obs_slots_traffic_controls_n - controls_written) * TRAFFIC_CONTROL_FEATURES;
 }
 
@@ -4763,6 +4741,10 @@ static void compute_observations(Drive *env) {
         obs_idx = write_partner_obs(env, ego, i, obs, obs_idx, &partner_count);
         obs_idx = write_road_obs(env, ego, obs, obs_idx, &lane_count, &boundary_count);
         obs_idx = write_traffic_control_obs(env, ego, obs, obs_idx, &traffic_control_count);
+        obs[obs_idx++] = (float) lane_count;
+        obs[obs_idx++] = (float) boundary_count;
+        obs[obs_idx++] = (float) partner_count;
+        obs[obs_idx++] = (float) traffic_control_count;
         assert(obs_idx == obs_per_agent);
     }
 }
diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py
index 577d42252e..1884eaed8a 100644
--- a/pufferlib/ocean/drive/drive.py
+++ b/pufferlib/ocean/drive/drive.py
@@ -81,7 +81,7 @@ def __init__(
         reward_conditioning=False,
         reward_randomization=False,
         compute_eval_metrics=True,
-        split_network=False,
+        shared_network=True,
         obs_slots_lane_n=32,
         obs_slots_boundary_n=32,
         obs_slots_partners_n=16,
@@ -113,7 +113,7 @@ def __init__(
         self.reward_conditioning = reward_conditioning
         self.reward_randomization = reward_randomization
         self.compute_eval_metrics = compute_eval_metrics
-        self.split_network = split_network
+        self.shared_network = shared_network
         self.render_mode = render_mode
         self.num_maps = num_maps
         self.report_interval = report_interval
@@ -210,6 +210,7 @@ def __init__(
         self.partner_features = binding.PARTNER_FEATURES
         self.road_features = binding.ROAD_FEATURES
         self.traffic_control_features = binding.TRAFFIC_CONTROL_FEATURES
+        self.obs_count_features = binding.OBS_COUNT_FEATURES
         self.num_reward_coefs = binding.NUM_REWARD_COEFS if reward_conditioning else 0
 
         # Target features based on target_type
@@ -227,6 +228,7 @@ def __init__(
             + self.obs_slots_lane_kept * self.road_features
             + self.obs_slots_boundary_kept * self.road_features
             + self.obs_slots_traffic_controls_n * self.traffic_control_features
+            + self.obs_count_features
         )
 
         self.single_observation_space = gymnasium.spaces.Box(low=-1, high=1, shape=(self.num_obs,), dtype=np.float32)
diff --git a/pufferlib/ocean/env_binding.h b/pufferlib/ocean/env_binding.h
index 9c6cb56f6b..52f22173ce 100644
--- a/pufferlib/ocean/env_binding.h
+++ b/pufferlib/ocean/env_binding.h
@@ -1380,6 +1380,7 @@ PyMODINIT_FUNC PyInit_binding(void) {
     PyModule_AddIntConstant(m, "ROAD_FEATURES", ROAD_FEATURES);
     PyModule_AddIntConstant(m, "PARTNER_FEATURES", PARTNER_FEATURES);
     PyModule_AddIntConstant(m, "TRAFFIC_CONTROL_FEATURES", TRAFFIC_CONTROL_FEATURES);
+    PyModule_AddIntConstant(m, "OBS_COUNT_FEATURES", OBS_COUNT_FEATURES);
     PyModule_AddIntConstant(m, "NUM_TRAFFIC_CONTROL_TYPES", NUM_TRAFFIC_CONTROL_TYPES);
     PyModule_AddIntConstant(m, "NUM_TRAFFIC_CONTROL_STATES", NUM_TRAFFIC_CONTROL_STATES);
     PyModule_AddIntConstant(m, "TRAFFIC_CONTROL_TYPE_NONE", TRAFFIC_CONTROL_TYPE_NONE);
diff --git a/pufferlib/ocean/torch.py b/pufferlib/ocean/torch.py
index c0e4f06cb7..d86edbce36 100644
--- a/pufferlib/ocean/torch.py
+++ b/pufferlib/ocean/torch.py
@@ -11,6 +11,8 @@
 
 Recurrent = pufferlib.models.LSTMWrapper
 
+ACTIVATIONS = {"relu": nn.ReLU, "tanh": nn.Tanh, "gelu": nn.GELU}
+
 
 class DriveBackbone(nn.Module):
     """
@@ -19,34 +21,55 @@ class DriveBackbone(nn.Module):
       - Split Actor/Critic (configurable)
     """
 
-    def _create_encoder(self, in_features, input_size, encoder_gigaflow, dropout=0.0):
-        if encoder_gigaflow:
-            return nn.Sequential(
-                pufferlib.pytorch.layer_init(nn.Linear(in_features, input_size)),
-                nn.LayerNorm(input_size),
-                nn.Tanh(),
-                nn.Dropout(dropout),
-                pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)),
-            )
-        else:
-            return nn.Sequential(
-                pufferlib.pytorch.layer_init(nn.Linear(in_features, input_size)),
-                nn.LayerNorm(input_size),
-                pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)),
-            )
+    def _create_encoder(self, in_features, out_size):
+        layers = [pufferlib.pytorch.layer_init(nn.Linear(in_features, out_size))]
+        if self.encoder_layer_norm:
+            layers.append(nn.LayerNorm(out_size))
+        layers.append(self.encoder_act_cls())
+        layers.append(pufferlib.pytorch.layer_init(nn.Linear(out_size, out_size)))
+        return nn.Sequential(*layers)
+
+    def _encode_and_pool(self, objects, valid_counts, encoder, out_size):
+        if not self.mask_padded_observations:
+            return encoder(objects).max(dim=1).values
+
+        valid_mask = torch.arange(objects.shape[1], device=objects.device) < valid_counts.unsqueeze(1)
+        encoded_objects = objects.new_full(
+            (objects.shape[0], objects.shape[1], out_size),
+            torch.finfo(objects.dtype).min,
+        )
+        encoded_objects[valid_mask] = encoder(objects[valid_mask])
+        pooled = encoded_objects.amax(dim=1)
+        return torch.where(valid_counts.unsqueeze(1) == 0, encoded_objects.new_zeros(()), pooled)
 
     def __init__(
         self,
         env,
-        input_size,
+        ego_input_size,
+        partner_input_size,
+        lane_input_size,
+        boundary_input_size,
+        traffic_control_input_size,
+        conditioning_input_size,
         backbone_hidden_size,
         backbone_num_layers,
         ego_dim,
-        encoder_gigaflow,
-        dropout,
+        encoder_activation,
+        encoder_layer_norm,
+        backbone_activation,
+        backbone_layer_norm,
+        mask_padded_observations,
     ):
         super().__init__()
-        self.input_size = input_size
+        self.encoder_act_cls = ACTIVATIONS[encoder_activation]
+        self.encoder_layer_norm = encoder_layer_norm
+        self.ego_dim = ego_dim
+        self.ego_input_size = ego_input_size
+        self.partner_input_size = partner_input_size
+        self.lane_input_size = lane_input_size
+        self.boundary_input_size = boundary_input_size
+        self.traffic_control_input_size = traffic_control_input_size
+        self.conditioning_input_size = conditioning_input_size
 
         # Observation dimensions from environment config
         self.obs_slots_partners_n = env.obs_slots_partners_n
@@ -64,55 +87,47 @@ def __init__(
             + binding.NUM_TRAFFIC_CONTROL_TYPES
             + binding.NUM_TRAFFIC_CONTROL_STATES
         )
+        self.obs_count_features = binding.OBS_COUNT_FEATURES
+        self.mask_padded_observations = mask_padded_observations
         # Conditioning size (reward coefficients + target info)
         self.conditioning_dim = env.num_reward_coefs + env.target_dim
 
-        num_feature_sets = 1
-
         # 1. observations Encoders
-        # Each encoder projects raw features into a common input_size embedding space
-        self.ego_encoder = self._create_encoder(ego_dim, input_size, encoder_gigaflow)
+        # Each encoder projects raw features into its own embedding space
+        self.ego_encoder = self._create_encoder(ego_dim, ego_input_size)
+        encoders_out = ego_input_size
         if self.obs_slots_lane_kept > 0:
-            self.lane_encoder = self._create_encoder(
-                self.road_features_count,
-                input_size,
-                encoder_gigaflow,
-                dropout=dropout,
-            )
-            num_feature_sets += 1
+            self.lane_encoder = self._create_encoder(self.road_features_count, lane_input_size)
+            encoders_out += lane_input_size
         if self.obs_slots_boundary_kept > 0:
-            self.boundary_encoder = self._create_encoder(
-                self.road_features_count,
-                input_size,
-                encoder_gigaflow,
-                dropout=dropout,
-            )
-            num_feature_sets += 1
+            self.boundary_encoder = self._create_encoder(self.road_features_count, boundary_input_size)
+            encoders_out += boundary_input_size
         if self.obs_slots_partners_n > 0:
-            self.partner_encoder = self._create_encoder(self.partner_features_count, input_size, encoder_gigaflow)
-            num_feature_sets += 1
+            self.partner_encoder = self._create_encoder(self.partner_features_count, partner_input_size)
+            encoders_out += partner_input_size
         if self.obs_slots_traffic_controls_n > 0:
             self.traffic_control_encoder = self._create_encoder(
-                self.traffic_control_features_after_onehot,
-                input_size,
-                encoder_gigaflow,
+                self.traffic_control_features_after_onehot, traffic_control_input_size
             )
-            num_feature_sets += 1
+            encoders_out += traffic_control_input_size
         if self.conditioning_dim > 0:
-            self.conditioning_encoder = self._create_encoder(self.conditioning_dim, input_size, encoder_gigaflow)
-            num_feature_sets += 1
+            self.conditioning_encoder = self._create_encoder(self.conditioning_dim, conditioning_input_size)
+            encoders_out += conditioning_input_size
 
         # 2. Main Backbone MLP
+        backbone_act_cls = ACTIVATIONS[backbone_activation]
         backbone_layers = []
-        bb_in = num_feature_sets * input_size
+        bb_in = encoders_out
         for _ in range(backbone_num_layers):
-            backbone_layers.append(nn.GELU())
+            backbone_layers.append(backbone_act_cls())
             backbone_layers.append(pufferlib.pytorch.layer_init(nn.Linear(bb_in, backbone_hidden_size)))
+            if backbone_layer_norm:
+                backbone_layers.append(nn.LayerNorm(backbone_hidden_size))
             bb_in = backbone_hidden_size
-        # Add final GELU before heads
-        backbone_layers.append(nn.GELU())
+        # Add final activation before heads
+        backbone_layers.append(backbone_act_cls())
         self.backbone = nn.Sequential(*backbone_layers)
-        self.out_dim = backbone_hidden_size if backbone_num_layers > 0 else num_feature_sets * input_size
+        self.out_dim = backbone_hidden_size if backbone_num_layers > 0 else encoders_out
 
     def forward(self, observations, ego_dim):
         # Extract and slice observations from the flat buffer
@@ -137,6 +152,20 @@ def forward(self, observations, ego_dim):
         slide_idx += boundary_dim
 
         traffic_control_observations = observations[:, slide_idx : slide_idx + traffic_control_dim]
+        count_observations = observations[
+            :, slide_idx + traffic_control_dim : slide_idx + traffic_control_dim + self.obs_count_features
+        ]
+        lane_counts, boundary_counts, partner_counts, traffic_control_counts = [
+            count_observations[:, i].long().clamp_(0, capacity)
+            for i, capacity in enumerate(
+                (
+                    self.obs_slots_lane_kept,
+                    self.obs_slots_boundary_kept,
+                    self.obs_slots_partners_n,
+                    self.obs_slots_traffic_controls_n,
+                )
+            )
+        ]
 
         # Encode Ego State
         ego_features = self.ego_encoder(ego_observations)
@@ -146,17 +175,27 @@ def forward(self, observations, ego_dim):
         # Encode Lanes and Boundaries separately
         if self.obs_slots_lane_kept > 0:
             lane_objects = lane_observations.view(-1, self.obs_slots_lane_kept, self.road_features_count)
-            lane_features = self.lane_encoder(lane_objects).max(dim=1).values
+            lane_features = self._encode_and_pool(lane_objects, lane_counts, self.lane_encoder, self.lane_input_size)
             feature_list.append(lane_features)
         if self.obs_slots_boundary_kept > 0:
             boundary_objects = boundary_observations.view(-1, self.obs_slots_boundary_kept, self.road_features_count)
-            boundary_features = self.boundary_encoder(boundary_objects).max(dim=1).values
+            boundary_features = self._encode_and_pool(
+                boundary_objects,
+                boundary_counts,
+                self.boundary_encoder,
+                self.boundary_input_size,
+            )
             feature_list.append(boundary_features)
 
         # Encode Partners
         if self.obs_slots_partners_n > 0:
             partner_objects = partner_observations.view(-1, self.obs_slots_partners_n, self.partner_features_count)
-            partner_features = self.partner_encoder(partner_objects).max(dim=1).values
+            partner_features = self._encode_and_pool(
+                partner_objects,
+                partner_counts,
+                self.partner_encoder,
+                self.partner_input_size,
+            )
             feature_list.append(partner_features)
 
         # Encode Traffic Controls
@@ -179,7 +218,12 @@ def forward(self, observations, ego_dim):
                 [traffic_control_continuous, traffic_control_type_onehot, traffic_control_state_onehot],
                 dim=2,
             )
-            traffic_control_features = self.traffic_control_encoder(traffic_control_objects).max(dim=1).values
+            traffic_control_features = self._encode_and_pool(
+                traffic_control_objects,
+                traffic_control_counts,
+                self.traffic_control_encoder,
+                self.traffic_control_input_size,
+            )
             feature_list.append(traffic_control_features)
 
         # Add optional features if enabled
@@ -263,43 +307,59 @@ class Drive(nn.Module):
     def __init__(
         self,
         env,
-        input_size: int,
+        ego_input_size: int,
+        partner_input_size: int,
+        lane_input_size: int,
+        boundary_input_size: int,
+        traffic_control_input_size: int,
+        conditioning_input_size: int,
         backbone_hidden_size: int,
         backbone_num_layers: int,
         actor_hidden_size: int,
         actor_num_layers: int,
         critic_hidden_size: int,
         critic_num_layers: int,
-        encoder_gigaflow: bool,
-        dropout: int,
-        split_network: bool,
+        encoder_activation: str,
+        encoder_layer_norm: bool,
+        backbone_activation: str,
+        backbone_layer_norm: bool,
+        shared_network: bool,
+        mask_padded_observations: bool = True,
     ):
         super().__init__()
 
         # Configuration flags from policy kwargs
-        self.split_network = split_network
+        self.shared_network = shared_network
         self.ego_dim = env.ego_features
 
         # Prepare arguments for the Backbone
         backbone_args = {
             "env": env,
-            "input_size": input_size,
+            "ego_input_size": ego_input_size,
+            "partner_input_size": partner_input_size,
+            "lane_input_size": lane_input_size,
+            "boundary_input_size": boundary_input_size,
+            "traffic_control_input_size": traffic_control_input_size,
+            "conditioning_input_size": conditioning_input_size,
             "backbone_hidden_size": backbone_hidden_size,
             "backbone_num_layers": backbone_num_layers,
             "ego_dim": self.ego_dim,
-            "encoder_gigaflow": encoder_gigaflow,
-            "dropout": dropout,
+            "encoder_activation": encoder_activation,
+            "encoder_layer_norm": encoder_layer_norm,
+            "backbone_activation": backbone_activation,
+            "backbone_layer_norm": backbone_layer_norm,
+            "mask_padded_observations": mask_padded_observations,
         }
 
         # Instantiate backbones
         self.actor_backbone = DriveBackbone(**backbone_args)
 
-        # If split_network is True, create a separate backbone for the critic.
-        # Otherwise, share the same backbone for both.
-        if self.split_network:
-            self.critic_backbone = DriveBackbone(**backbone_args)
-        else:
+        # If using shared network, critic backbone is the same as actor backbone.
+        # Otherwise, create a separate critic backbone with the same architecture.
+        if self.shared_network:
             self.critic_backbone = self.actor_backbone
+        else:
+            self.critic_backbone = DriveBackbone(**backbone_args)
 
         # Setup action and value heads
         self.is_continuous = isinstance(env.single_action_space, pufferlib.spaces.Box)
@@ -337,10 +397,10 @@ def forward(self, observations, state=None):
         actor_hidden = self.actor_backbone(observations, self.ego_dim)
 
         # Forward pass for critic (may use separate backbone)
-        if self.split_network:
-            critic_hidden = self.critic_backbone(observations, self.ego_dim)
-        else:
+        if self.shared_network:
             critic_hidden = actor_hidden
+        else:
+            critic_hidden = self.critic_backbone(observations, self.ego_dim)
 
         # Compute actions
         if self.is_continuous:
@@ -367,7 +427,7 @@ def pool_slot_counts(self, observations, state=None):
 
     # Required for PufferLib recurrent wrappers
     def encode_observations(self, observations, state=None):
-        assert not self.split_network, "LSTM wrapper doesn't support split_network=True"
+        assert self.shared_network, "LSTM wrapper requires shared_network=True"
         return self.actor_backbone(observations, self.ego_dim)
 
     def decode_actions(self, hidden):

From a9952e57cc271c361a51c4b051869182323d4b7f Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Tue, 2 Jun 2026 23:32:37 +0200
Subject: [PATCH 02/19] Update tests

---
 .../data/drive_rollout_golden.json            | 14 ++++-
 .../smoke_tests/data/drive_smoke_golden.json  | 57 ++++++++++++-------
 tests/smoke_tests/test_drive_train.py         |  7 ++-
 3 files changed, 55 insertions(+), 23 deletions(-)

diff --git a/tests/smoke_tests/data/drive_rollout_golden.json b/tests/smoke_tests/data/drive_rollout_golden.json
index 8f62e6e37b..0c5a06b948 100644
--- a/tests/smoke_tests/data/drive_rollout_golden.json
+++ b/tests/smoke_tests/data/drive_rollout_golden.json
@@ -6,12 +6,24 @@
     "comfort_violation_count": 0.7257332022373493,
     "dnf_rate": 0.5625,
     "episode_length": 46.42307692307692,
-    "episode_return": -2.3540108112188487,
+    "episode_return": -2.4922777001674357,
     "lane_center_rate": 0.7063253728243021,
     "n": 16.0,
     "num_goals_reached": 0.0,
     "offroad_rate": 0.3894230769230769,
     "red_light_violation_rate": 0.038461538461538464,
+    "reward_components/ade": 0.0,
+    "reward_components/collision": -0.015820913589917697,
+    "reward_components/comfort": -1.690985042315263,
+    "reward_components/goal": 0.0,
+    "reward_components/lane_align": -0.14203165815426752,
+    "reward_components/lane_center": -0.0026557931694417046,
+    "reward_components/offroad": -0.5841346153846154,
+    "reward_components/overspeed": 0.0,
+    "reward_components/red_light": -0.038461538461538464,
+    "reward_components/reverse": -0.01809134094331127,
+    "reward_components/timestep": -0.0001000300175152146,
+    "reward_components/velocity": 2.3821118632510593e-06,
     "score": 0.0,
     "velocity_progress_sum": 0.00018323936428015048
   },
diff --git a/tests/smoke_tests/data/drive_smoke_golden.json b/tests/smoke_tests/data/drive_smoke_golden.json
index 2825c653ba..b3e2c6865e 100644
--- a/tests/smoke_tests/data/drive_smoke_golden.json
+++ b/tests/smoke_tests/data/drive_smoke_golden.json
@@ -1,33 +1,48 @@
 {
   "env": {
-    "avg_distance_per_infraction": 13.20275001525879,
-    "avg_speed_per_agent": 1.3609784364700317,
-    "collision_rate": 0.0125,
-    "comfort_violation_count": 0.7306405305862427,
-    "dnf_rate": 0.55,
-    "episode_length": 43.6,
-    "episode_return": -2.2548463344573975,
-    "lane_center_rate": 0.6961542010307312,
+    "avg_distance_per_infraction": 13.18341121673584,
+    "avg_speed_per_agent": 1.3711345672607422,
+    "collision_rate": 0.0375,
+    "comfort_violation_count": 0.7263497829437255,
+    "dnf_rate": 0.5625,
+    "episode_length": 41.8,
+    "episode_return": -2.334346318244934,
+    "lane_center_rate": 0.7175199031829834,
     "n": 16.0,
     "num_goals_reached": 0.0,
-    "offroad_rate": 0.3625,
-    "red_light_violation_rate": 0.075,
+    "obs/max": 80.0,
+    "obs/mean": 0.2281341243069619,
+    "obs/min": -1.236907229758799,
+    "offroad_rate": 0.375,
+    "red_light_violation_rate": 0.025,
+    "reward_components/ade": 0.0,
+    "reward_components/collision": -0.06195625364780426,
+    "reward_components/comfort": -1.5356246471405028,
+    "reward_components/goal": 0.0,
+    "reward_components/lane_align": -0.13004764765501023,
+    "reward_components/lane_center": -0.002508045267313719,
+    "reward_components/offroad": -0.5625,
+    "reward_components/overspeed": 0.0,
+    "reward_components/red_light": -0.025,
+    "reward_components/reverse": -0.01661874633282423,
+    "reward_components/timestep": -9.165622614091263e-05,
+    "reward_components/velocity": 0.0,
     "score": 0.0,
     "velocity_progress_sum": 0.0
   },
   "losses": {
-    "approx_kl": 0.0004938042755903942,
+    "approx_kl": 0.0002155543354872082,
     "clipfrac": 0.0,
-    "ema_max": 1.191539317369461,
-    "entropy": 2.4832939420427596,
-    "explained_variance": 0.18538302183151245,
-    "filter_threshold": 0.01191539317369461,
-    "filtered_fraction": 0.023398328690807824,
-    "kept_fraction": 0.9766016713091922,
-    "masked_fraction": 0.12353515625,
-    "old_approx_kl": 0.000515544121818883,
-    "policy_loss": -0.0005634417258469122,
-    "value_loss": 0.08380150688546044
+    "ema_max": 1.2262159883975983,
+    "entropy": 2.484058209827968,
+    "explained_variance": 0.16326427459716797,
+    "filter_threshold": 0.012262159883975983,
+    "filtered_fraction": 0.024684585847504104,
+    "kept_fraction": 0.9753154141524959,
+    "masked_fraction": 0.10986328125,
+    "old_approx_kl": -0.0011627360114029475,
+    "policy_loss": -0.0004513516489948545,
+    "value_loss": 0.08172488159367017
   },
   "meta": {
     "bptt_horizon": 64,
diff --git a/tests/smoke_tests/test_drive_train.py b/tests/smoke_tests/test_drive_train.py
index 038e974211..554d1875ae 100644
--- a/tests/smoke_tests/test_drive_train.py
+++ b/tests/smoke_tests/test_drive_train.py
@@ -148,7 +148,12 @@ def _build_config():
     _set_existing(
         args["policy"],
         {
-            "input_size": 32,
+            "ego_input_size": 32,
+            "partner_input_size": 32,
+            "lane_input_size": 32,
+            "boundary_input_size": 32,
+            "traffic_control_input_size": 32,
+            "conditioning_input_size": 32,
             "backbone_hidden_size": 32,
             "actor_hidden_size": 32,
             "critic_hidden_size": 32,

From 212fd0e3e4d79e8a546bc8d0a1fcd7891beca7cd Mon Sep 17 00:00:00 2001
From: Valentin Charraut <16002514+vcharraut@users.noreply.github.com>
Date: Tue, 2 Jun 2026 23:42:41 +0200
Subject: [PATCH 03/19] Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---
 notebooks/01_observations.ipynb | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/notebooks/01_observations.ipynb b/notebooks/01_observations.ipynb
index 7931e59af9..3197e06c39 100644
--- a/notebooks/01_observations.ipynb
+++ b/notebooks/01_observations.ipynb
@@ -165,8 +165,7 @@
     "idx += env.obs_slots_traffic_controls_n * env.traffic_control_features\n",
     "assert np.allclose(traffic_manual, traffic), \"traffic mismatch\"\n",
     "\n",
-    "idx += 4  # padding at end of obs\n",
-    "\n",
+    idx += 4  # appended slot-count features at end of obs
     "assert idx == obs.shape[1], f\"obs size mismatch: used {idx}, total {obs.shape[1]}\"\n",
     "print(f\"All slices match. Total features used: {idx}\")"
    ]

From b3c91f0045dc8ed91bf503f676579fc3828ca888 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Tue, 2 Jun 2026 23:45:55 +0200
Subject: [PATCH 04/19] Rename mask_padded_observations to mask_padded_features
 for consistency

---
 pufferlib/config/ocean/drive.ini |  2 +-
 pufferlib/ocean/torch.py         | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini
index 63d87ac303..0740b5896b 100644
--- a/pufferlib/config/ocean/drive.ini
+++ b/pufferlib/config/ocean/drive.ini
@@ -21,7 +21,7 @@ conditioning_input_size = 64
 ; Encoder activation - options: "relu", "tanh", "gelu"
 encoder_activation = "relu"
 encoder_layer_norm = True
-mask_padded_observations = False
+mask_padded_features = False
 ; Shared backbone layer
 backbone_hidden_size = 512
 backbone_num_layers = 4
diff --git a/pufferlib/ocean/torch.py b/pufferlib/ocean/torch.py
index d86edbce36..1bc828bf5e 100644
--- a/pufferlib/ocean/torch.py
+++ b/pufferlib/ocean/torch.py
@@ -30,7 +30,7 @@ def _create_encoder(self, in_features, out_size):
         return nn.Sequential(*layers)
 
     def _encode_and_pool(self, objects, valid_counts, encoder, out_size):
-        if not self.mask_padded_observations:
+        if not self.mask_padded_features:
             return encoder(objects).max(dim=1).values
 
         valid_mask = torch.arange(objects.shape[1], device=objects.device) < valid_counts.unsqueeze(1)
@@ -58,7 +58,7 @@ def __init__(
         encoder_layer_norm,
         backbone_activation,
         backbone_layer_norm,
-        mask_padded_observations,
+        mask_padded_features,
     ):
         super().__init__()
         self.encoder_act_cls = ACTIVATIONS[encoder_activation]
@@ -88,7 +88,7 @@ def __init__(
             + binding.NUM_TRAFFIC_CONTROL_STATES
         )
         self.obs_count_features = binding.OBS_COUNT_FEATURES
-        self.mask_padded_observations = mask_padded_observations
+        self.mask_padded_features = mask_padded_features
         # Conditioning size (reward coefficients + target info)
         self.conditioning_dim = env.num_reward_coefs + env.target_dim
 
@@ -324,7 +324,7 @@ def __init__(
         backbone_activation: str,
         backbone_layer_norm: bool,
         shared_network: bool,
-        mask_padded_observations: bool = True,
+        mask_padded_features: bool,
     ):
         super().__init__()
 
@@ -348,7 +348,7 @@ def __init__(
             "encoder_layer_norm": encoder_layer_norm,
             "backbone_activation": backbone_activation,
             "backbone_layer_norm": backbone_layer_norm,
-            "mask_padded_observations": mask_padded_observations,
+            "mask_padded_features": mask_padded_features,
         }
 
         # Instantiate backbones

From 718613194e02c619c55bdd5f669b0fb1e3569917 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Tue, 2 Jun 2026 23:52:48 +0200
Subject: [PATCH 05/19] Rename OBS_COUNT_FEATURES to OBS_SLOT_NUM_TYPES for
 consistency across the codebase

---
 pufferlib/ocean/drive/drive.h  | 4 ++--
 pufferlib/ocean/drive/drive.py | 4 ++--
 pufferlib/ocean/env_binding.h  | 2 +-
 pufferlib/ocean/torch.py       | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h
index 2397310f36..5303799b1a 100644
--- a/pufferlib/ocean/drive/drive.h
+++ b/pufferlib/ocean/drive/drive.h
@@ -121,9 +121,9 @@
 #define ROAD_FEATURES 7
 #define PARTNER_FEATURES 9
 #define TRAFFIC_CONTROL_FEATURES 7
-#define OBS_COUNT_FEATURES 4
 #define STATIC_TARGET_FEATURES 3
 #define DYNAMIC_TARGET_FEATURES 5
+#define OBS_SLOT_NUM_TYPES 4
 
 // GIGAFLOW specific
 #define MAX_ROUTE_LENGTH 64
@@ -3700,7 +3700,7 @@ static int compute_observation_size(Drive *env) {
                                                               : 0;
     return EGO_FEATURES + PARTNER_FEATURES * env->obs_slots_partners_n
         + ROAD_FEATURES * (env->obs_slots_lane_kept + env->obs_slots_boundary_kept)
-        + TRAFFIC_CONTROL_FEATURES * env->obs_slots_traffic_controls_n + OBS_COUNT_FEATURES
+        + TRAFFIC_CONTROL_FEATURES * env->obs_slots_traffic_controls_n + OBS_SLOT_NUM_TYPES
         + env->reward_conditioning * NUM_REWARD_COEFS + env->num_target_waypoints * target_features;
 }
 
diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py
index 1884eaed8a..59ed0aa3eb 100644
--- a/pufferlib/ocean/drive/drive.py
+++ b/pufferlib/ocean/drive/drive.py
@@ -210,7 +210,7 @@ def __init__(
         self.partner_features = binding.PARTNER_FEATURES
         self.road_features = binding.ROAD_FEATURES
         self.traffic_control_features = binding.TRAFFIC_CONTROL_FEATURES
-        self.obs_count_features = binding.OBS_COUNT_FEATURES
+        self.obs_slot_num_types = binding.OBS_SLOT_NUM_TYPES
         self.num_reward_coefs = binding.NUM_REWARD_COEFS if reward_conditioning else 0
 
         # Target features based on target_type
@@ -228,7 +228,7 @@ def __init__(
             + self.obs_slots_lane_kept * self.road_features
             + self.obs_slots_boundary_kept * self.road_features
             + self.obs_slots_traffic_controls_n * self.traffic_control_features
-            + self.obs_count_features
+            + self.obs_slot_num_types
         )
 
         self.single_observation_space = gymnasium.spaces.Box(low=-1, high=1, shape=(self.num_obs,), dtype=np.float32)
diff --git a/pufferlib/ocean/env_binding.h b/pufferlib/ocean/env_binding.h
index 52f22173ce..a45038d8b5 100644
--- a/pufferlib/ocean/env_binding.h
+++ b/pufferlib/ocean/env_binding.h
@@ -1380,7 +1380,7 @@ PyMODINIT_FUNC PyInit_binding(void) {
     PyModule_AddIntConstant(m, "ROAD_FEATURES", ROAD_FEATURES);
     PyModule_AddIntConstant(m, "PARTNER_FEATURES", PARTNER_FEATURES);
     PyModule_AddIntConstant(m, "TRAFFIC_CONTROL_FEATURES", TRAFFIC_CONTROL_FEATURES);
-    PyModule_AddIntConstant(m, "OBS_COUNT_FEATURES", OBS_COUNT_FEATURES);
+    PyModule_AddIntConstant(m, "OBS_SLOT_NUM_TYPES", OBS_SLOT_NUM_TYPES);
     PyModule_AddIntConstant(m, "NUM_TRAFFIC_CONTROL_TYPES", NUM_TRAFFIC_CONTROL_TYPES);
     PyModule_AddIntConstant(m, "NUM_TRAFFIC_CONTROL_STATES", NUM_TRAFFIC_CONTROL_STATES);
     PyModule_AddIntConstant(m, "TRAFFIC_CONTROL_TYPE_NONE", TRAFFIC_CONTROL_TYPE_NONE);
diff --git a/pufferlib/ocean/torch.py b/pufferlib/ocean/torch.py
index 1bc828bf5e..1c1f265d4b 100644
--- a/pufferlib/ocean/torch.py
+++ b/pufferlib/ocean/torch.py
@@ -87,7 +87,7 @@ def __init__(
             + binding.NUM_TRAFFIC_CONTROL_TYPES
             + binding.NUM_TRAFFIC_CONTROL_STATES
         )
-        self.obs_count_features = binding.OBS_COUNT_FEATURES
+        self.obs_slot_num_types = binding.OBS_SLOT_NUM_TYPES
         self.mask_padded_features = mask_padded_features
         # Conditioning size (reward coefficients + target info)
         self.conditioning_dim = env.num_reward_coefs + env.target_dim
@@ -153,7 +153,7 @@ def forward(self, observations, ego_dim):
 
         traffic_control_observations = observations[:, slide_idx : slide_idx + traffic_control_dim]
         count_observations = observations[
-            :, slide_idx + traffic_control_dim : slide_idx + traffic_control_dim + self.obs_count_features
+            :, slide_idx + traffic_control_dim : slide_idx + traffic_control_dim + self.obs_slot_num_types
         ]
         lane_counts, boundary_counts, partner_counts, traffic_control_counts = [
             count_observations[:, i].long().clamp_(0, capacity)

From e1d817f51f6d93c0cd930df780e2bd74243ede24 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Wed, 3 Jun 2026 00:23:22 +0200
Subject: [PATCH 06/19] Refactor conditioning terminology to target in
 training, inference, architecture, and utility files

- Updated references from `conditioning_dim` to `target_dim` in training and inference notebooks.
- Changed `conditioning_input_size` to `target_input_size` in configuration files and utility scripts.
- Adjusted encoder creation and usage in the DriveBackbone class to reflect the new target terminology.
- Ensured consistency across all relevant files to improve clarity and maintainability.
---
 notebooks/04_training.ipynb           |  4 ++--
 notebooks/05_inference.ipynb          |  6 +++---
 notebooks/06_architecture.ipynb       | 18 ++++++++---------
 notebooks/notebook_utils.py           |  2 +-
 pufferlib/config/ocean/drive.ini      |  2 +-
 pufferlib/ocean/torch.py              | 28 +++++++++++++--------------
 tests/smoke_tests/test_drive_train.py |  2 +-
 7 files changed, 31 insertions(+), 31 deletions(-)

diff --git a/notebooks/04_training.ipynb b/notebooks/04_training.ipynb
index 7a79a8e0ae..b1da07df42 100644
--- a/notebooks/04_training.ipynb
+++ b/notebooks/04_training.ipynb
@@ -146,7 +146,7 @@
     "    f\"ego_obs: shape={ego_obs.shape}, NaN={torch.isnan(ego_obs).sum().item()}, range=[{ego_obs.min():.3f}, {ego_obs.max():.3f}]\"\n",
     ")\n",
     "\n",
-    "cond_dim = backbone.conditioning_dim\n",
+    "cond_dim = backbone.target_dim\n",
     "if cond_dim > 0:\n",
     "    cond_obs = x[:, slide_idx : slide_idx + cond_dim]\n",
     "    slide_idx += cond_dim\n",
@@ -180,7 +180,7 @@
     "\n",
     "if cond_dim > 0:\n",
     "    with torch.no_grad():\n",
-    "        cond_enc = backbone.conditioning_encoder(cond_obs)\n",
+    "        cond_enc = backbone.target_encoder(cond_obs)\n",
     "    print(\n",
     "        f\"{'cond':>10s}_enc: NaN={torch.isnan(cond_enc).sum().item()}, dead={((cond_enc.abs().sum(dim=0) == 0).sum().item())}, range=[{cond_enc.min():.3f}, {cond_enc.max():.3f}]\"\n",
     "    )"
diff --git a/notebooks/05_inference.ipynb b/notebooks/05_inference.ipynb
index 9e2653d828..c605aaffee 100644
--- a/notebooks/05_inference.ipynb
+++ b/notebooks/05_inference.ipynb
@@ -1514,8 +1514,8 @@
     "            True,\n",
     "        )\n",
     "    )\n",
-    "if bb.conditioning_dim > 0:\n",
-    "    enc_inventory.append((\"conditioning\", bb.conditioning_encoder, bb.conditioning_dim, 1, False))\n",
+    "if bb.target_dim > 0:\n",
+    "    enc_inventory.append((\"conditioning\", bb.target_encoder, bb.target_dim, 1, False))\n",
     "\n",
     "enc_names = [n for n, *_ in enc_inventory]\n",
     "set_encs = [n for n, _, _, _, is_set in enc_inventory if is_set]\n",
@@ -1554,7 +1554,7 @@
     "lane_dim = bb.obs_slots_lane_kept * bb.road_features_count\n",
     "boundary_dim = bb.obs_slots_boundary_kept * bb.road_features_count\n",
     "traffic_dim = bb.obs_slots_traffic_controls_n * bb.traffic_control_features_count\n",
-    "_s = ego_dim + bb.conditioning_dim\n",
+    "_s = ego_dim + bb.target_dim\n",
     "sl = {}\n",
     "sl[\"partner\"] = (_s, _s + partner_dim, bb.obs_slots_partners_n, bb.partner_features_count)\n",
     "_s += partner_dim\n",
diff --git a/notebooks/06_architecture.ipynb b/notebooks/06_architecture.ipynb
index 260b636444..7a799a2abf 100644
--- a/notebooks/06_architecture.ipynb
+++ b/notebooks/06_architecture.ipynb
@@ -47,7 +47,7 @@
     "    lane_input_size=INPUT_SIZE,\n",
     "    boundary_input_size=INPUT_SIZE,\n",
     "    traffic_control_input_size=INPUT_SIZE,\n",
-    "    conditioning_input_size=INPUT_SIZE,\n",
+    "    target_input_size=INPUT_SIZE,\n",
     "    backbone_hidden_size=BACKBONE_HIDDEN_SIZE,\n",
     "    backbone_num_layers=BACKBONE_NUM_LAYERS,\n",
     "    actor_hidden_size=ACTOR_HIDDEN_SIZE,\n",
@@ -102,7 +102,7 @@
    "outputs": [],
    "source": [
     "backbone = policy.actor_backbone\n",
-    "cond_dim = backbone.conditioning_dim\n",
+    "cond_dim = backbone.target_dim\n",
     "\n",
     "# Collect encoder info\n",
     "encoders = [\n",
@@ -239,8 +239,8 @@
     "    \"partner_encoder\": backbone.partner_encoder,\n",
     "    \"traffic_ctrl_encoder\": backbone.traffic_control_encoder,\n",
     "}\n",
-    "if backbone.conditioning_dim > 0:\n",
-    "    components[\"conditioning_encoder\"] = backbone.conditioning_encoder\n",
+    "if backbone.target_dim > 0:\n",
+    "    components[\"target_encoder\"] = backbone.target_encoder\n",
     "components[\"backbone_mlp\"] = backbone.backbone\n",
     "components[\"actor_head\"] = policy.actor_head\n",
     "components[\"critic_head\"] = policy.critic_head\n",
@@ -287,7 +287,7 @@
     "backbone = policy.actor_backbone\n",
     "\n",
     "slide_idx = env.ego_features\n",
-    "cond_dim = backbone.conditioning_dim\n",
+    "cond_dim = backbone.target_dim\n",
     "partner_dim = env.obs_slots_partners_n * env.partner_features\n",
     "lane_dim = env.obs_slots_lane_kept * env.road_features\n",
     "boundary_dim = env.obs_slots_boundary_kept * env.road_features\n",
@@ -330,7 +330,7 @@
     "    print(f\"  ego_encoder:     {ego_obs.shape} -> {ego_enc.shape}\")\n",
     "\n",
     "    if cond_dim > 0:\n",
-    "        cond_enc = backbone.conditioning_encoder(cond_obs)\n",
+    "        cond_enc = backbone.target_encoder(cond_obs)\n",
     "        print(f\"  cond_encoder:    {cond_obs.shape} -> {cond_enc.shape}\")\n",
     "\n",
     "    p_reshaped = partner_obs.view(-1, env.obs_slots_partners_n, env.partner_features)\n",
@@ -436,7 +436,7 @@
     "    activations[\"ego\"] = backbone.ego_encoder(obs_tensor[:, : env.ego_features])\n",
     "\n",
     "    if cond_dim > 0:\n",
-    "        activations[\"conditioning\"] = backbone.conditioning_encoder(obs_tensor[:, slide : slide + cond_dim])\n",
+    "        activations[\"conditioning\"] = backbone.target_encoder(obs_tensor[:, slide : slide + cond_dim])\n",
     "        slide += cond_dim\n",
     "\n",
     "    p_obs = obs_tensor[:, slide : slide + partner_dim].view(-1, env.obs_slots_partners_n, env.partner_features)\n",
@@ -580,7 +580,7 @@
     "    \"lane_input_size\": 64,\n",
     "    \"boundary_input_size\": 64,\n",
     "    \"traffic_control_input_size\": 64,\n",
-    "    \"conditioning_input_size\": 64,\n",
+    "    \"target_input_size\": 64,\n",
     "    \"backbone_num_layers\": 1,\n",
     "    \"actor_hidden_size\": 128,\n",
     "    \"actor_num_layers\": 0,\n",
@@ -605,7 +605,7 @@
     "            \"lane_input_size\": encoder_size,\n",
     "            \"boundary_input_size\": encoder_size,\n",
     "            \"traffic_control_input_size\": encoder_size,\n",
-    "            \"conditioning_input_size\": encoder_size,\n",
+    "            \"target_input_size\": encoder_size,\n",
     "        }\n",
     "    )\n",
     "    p = DrivePolicy(env, **full_cfg).to(device)\n",
diff --git a/notebooks/notebook_utils.py b/notebooks/notebook_utils.py
index 100d15c011..f7f61f459c 100644
--- a/notebooks/notebook_utils.py
+++ b/notebooks/notebook_utils.py
@@ -90,7 +90,7 @@
     "lane_input_size": 64,
     "boundary_input_size": 64,
     "traffic_control_input_size": 64,
-    "conditioning_input_size": 64,
+    "target_input_size": 64,
     "backbone_hidden_size": 128,
     "backbone_num_layers": 1,
     "actor_hidden_size": 128,
diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini
index 0740b5896b..dfff41aa3b 100644
--- a/pufferlib/config/ocean/drive.ini
+++ b/pufferlib/config/ocean/drive.ini
@@ -17,7 +17,7 @@ partner_input_size = 64
 lane_input_size = 64
 boundary_input_size = 64
 traffic_control_input_size = 64
-conditioning_input_size = 64
+target_input_size = 64
 ; Encoder activation - options: "relu", "tanh", "gelu"
 encoder_activation = "relu"
 encoder_layer_norm = True
diff --git a/pufferlib/ocean/torch.py b/pufferlib/ocean/torch.py
index 1c1f265d4b..3e33d32420 100644
--- a/pufferlib/ocean/torch.py
+++ b/pufferlib/ocean/torch.py
@@ -50,7 +50,7 @@ def __init__(
         lane_input_size,
         boundary_input_size,
         traffic_control_input_size,
-        conditioning_input_size,
+        target_input_size,
         backbone_hidden_size,
         backbone_num_layers,
         ego_dim,
@@ -69,7 +69,7 @@ def __init__(
         self.lane_input_size = lane_input_size
         self.boundary_input_size = boundary_input_size
         self.traffic_control_input_size = traffic_control_input_size
-        self.conditioning_input_size = conditioning_input_size
+        self.target_input_size = target_input_size
 
         # Observation dimensions from environment config
         self.obs_slots_partners_n = env.obs_slots_partners_n
@@ -90,7 +90,7 @@ def __init__(
         self.obs_slot_num_types = binding.OBS_SLOT_NUM_TYPES
         self.mask_padded_features = mask_padded_features
         # Conditioning size (reward coefficients + target info)
-        self.conditioning_dim = env.num_reward_coefs + env.target_dim
+        self.target_dim = env.num_reward_coefs + env.goal_dim
 
         # 1. observations Encoders
         # Each encoder projects raw features into its own embedding space
@@ -110,9 +110,9 @@ def __init__(
                 self.traffic_control_features_after_onehot, traffic_control_input_size
             )
             encoders_out += traffic_control_input_size
-        if self.conditioning_dim > 0:
-            self.conditioning_encoder = self._create_encoder(self.conditioning_dim, conditioning_input_size)
-            encoders_out += conditioning_input_size
+        if self.target_dim > 0:
+            self.target_encoder = self._create_encoder(self.target_dim, target_input_size)
+            encoders_out += target_input_size
 
         # 2. Main Backbone MLP
         backbone_act_cls = ACTIVATIONS[backbone_activation]
@@ -139,8 +139,8 @@ def forward(self, observations, ego_dim):
         slide_idx = ego_dim
         ego_observations = observations[:, :slide_idx]
 
-        conditioning_observations = observations[:, slide_idx : slide_idx + self.conditioning_dim]
-        slide_idx += self.conditioning_dim
+        target_observations = observations[:, slide_idx : slide_idx + self.target_dim]
+        slide_idx += self.target_dim
 
         partner_observations = observations[:, slide_idx : slide_idx + partner_dim]
         slide_idx += partner_dim
@@ -227,9 +227,9 @@ def forward(self, observations, ego_dim):
             feature_list.append(traffic_control_features)
 
         # Add optional features if enabled
-        if self.conditioning_dim > 0:
-            conditioning_features = self.conditioning_encoder(conditioning_observations)
-            feature_list.append(conditioning_features)
+        if self.target_dim > 0:
+            target_features = self.target_encoder(target_observations)
+            feature_list.append(target_features)
 
         # Concatenate all features and pass through main backbone
         concat_features = torch.cat(feature_list, dim=1)
@@ -241,7 +241,7 @@ def pool_slot_counts(self, observations, ego_dim):
         boundary_dim = self.obs_slots_boundary_kept * self.road_features_count
         traffic_control_dim = self.obs_slots_traffic_controls_n * self.traffic_control_features_count
 
-        slide_idx = ego_dim + self.conditioning_dim
+        slide_idx = ego_dim + self.target_dim
         partner_observations = observations[:, slide_idx : slide_idx + partner_dim]
         slide_idx += partner_dim
         lane_observations = observations[:, slide_idx : slide_idx + lane_dim]
@@ -312,7 +312,7 @@ def __init__(
         lane_input_size: int,
         boundary_input_size: int,
         traffic_control_input_size: int,
-        conditioning_input_size: int,
+        target_input_size: int,
         backbone_hidden_size: int,
         backbone_num_layers: int,
         actor_hidden_size: int,
@@ -340,7 +340,7 @@ def __init__(
             "lane_input_size": lane_input_size,
             "boundary_input_size": boundary_input_size,
             "traffic_control_input_size": traffic_control_input_size,
-            "conditioning_input_size": conditioning_input_size,
+            "target_input_size": target_input_size,
             "backbone_hidden_size": backbone_hidden_size,
             "backbone_num_layers": backbone_num_layers,
             "ego_dim": self.ego_dim,
diff --git a/tests/smoke_tests/test_drive_train.py b/tests/smoke_tests/test_drive_train.py
index 554d1875ae..a0aba76048 100644
--- a/tests/smoke_tests/test_drive_train.py
+++ b/tests/smoke_tests/test_drive_train.py
@@ -153,7 +153,7 @@ def _build_config():
             "lane_input_size": 32,
             "boundary_input_size": 32,
             "traffic_control_input_size": 32,
-            "conditioning_input_size": 32,
+            "target_input_size": 32,
             "backbone_hidden_size": 32,
             "actor_hidden_size": 32,
             "critic_hidden_size": 32,

From 77ca722fdf34b7c1ef576a1f806cc42508688401 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Wed, 3 Jun 2026 17:31:16 +0200
Subject: [PATCH 07/19] Add neural network architecture notebook and initialize
 notebooks package

- Created a new notebook `06_architecture.py` for visualizing and analyzing the DrivePolicy architecture, including model summary, encoder breakdown, forward pass tracing, weight distributions, and architecture comparisons.
- Initialized the `notebooks` package with an empty `__init__.py` file.
---
 notebooks/01_observations.ipynb |  485 ---------
 notebooks/01_observations.py    |  375 +++++++
 notebooks/02_rewards.ipynb      |  332 ------
 notebooks/02_rewards.py         |  241 +++++
 notebooks/03_metrics.ipynb      |  330 ------
 notebooks/03_metrics.py         |  235 ++++
 notebooks/04_training.ipynb     |  549 ----------
 notebooks/04_training.py        |  418 ++++++++
 notebooks/05_inference.ipynb    | 1782 -------------------------------
 notebooks/05_inference.py       | 1576 +++++++++++++++++++++++++++
 notebooks/06_architecture.ipynb |  813 --------------
 notebooks/06_architecture.py    |  694 ++++++++++++
 12 files changed, 3539 insertions(+), 4291 deletions(-)
 delete mode 100644 notebooks/01_observations.ipynb
 create mode 100644 notebooks/01_observations.py
 delete mode 100644 notebooks/02_rewards.ipynb
 create mode 100644 notebooks/02_rewards.py
 delete mode 100644 notebooks/03_metrics.ipynb
 create mode 100644 notebooks/03_metrics.py
 delete mode 100644 notebooks/04_training.ipynb
 create mode 100644 notebooks/04_training.py
 delete mode 100644 notebooks/05_inference.ipynb
 create mode 100644 notebooks/05_inference.py
 delete mode 100644 notebooks/06_architecture.ipynb
 create mode 100644 notebooks/06_architecture.py

diff --git a/notebooks/01_observations.ipynb b/notebooks/01_observations.ipynb
deleted file mode 100644
index 3197e06c39..0000000000
--- a/notebooks/01_observations.ipynb
+++ /dev/null
@@ -1,485 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 01 - Observation Pipeline Debug\n",
-    "Verify obs vector is correctly packed, normalized, interpretable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pufferlib.viz import plot_observation, plot_simulator_state, unpack_obs\n",
-    "from notebooks.notebook_utils import COEF_NAMES, make_drive_env, zero_actions\n",
-    "\n",
-    "env, obs, info = make_drive_env()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Raw obs inspection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Take first step so obs are populated\n",
-    "actions = zero_actions(env)\n",
-    "\n",
-    "obs, rew, term, trunc, info = env.step(actions)\n",
-    "\n",
-    "print(f\"shape: {obs.shape}, dtype: {obs.dtype}\")\n",
-    "print(f\"min: {obs.min():.4f}, max: {obs.max():.4f}, mean: {obs.mean():.4f}, std: {obs.std():.4f}\")\n",
-    "print(f\"NaN: {np.isnan(obs).sum()}, Inf: {np.isinf(obs).sum()}\")\n",
-    "print(f\"% zeros: {(obs == 0).mean() * 100:.1f}%\")\n",
-    "print(f\"% outside [-1,1]: {((obs < -1) | (obs > 1)).mean() * 100:.2f}%\")\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
-    "axes[0].hist(obs.flatten(), bins=100, edgecolor=\"black\", alpha=0.7)\n",
-    "axes[0].set_title(\"Full obs distribution\")\n",
-    "axes[0].set_xlabel(\"Value\")\n",
-    "# Per-agent: show obs[0] vs obs[1]\n",
-    "for i in range(min(4, obs.shape[0])):\n",
-    "    axes[1].plot(obs[i], alpha=0.5, label=f\"agent {i}\")\n",
-    "axes[1].set_title(\"Obs vector by index (first 4 agents)\")\n",
-    "axes[1].legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Unpack with pufferlib.viz.unpack_obs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ego, target, partners, lanes, boundaries, traffic = unpack_obs(\n",
-    "    obs[:1],\n",
-    "    target_type=env.target_type,\n",
-    "    reward_conditioning=env.reward_conditioning,\n",
-    "    num_target_waypoints=env.num_target_waypoints,\n",
-    "    obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "    obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "    obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    ")\n",
-    "print(f\"ego: {ego.shape} = {ego}\")\n",
-    "print(f\"target: {target.shape}\")\n",
-    "print(f\"partners: {partners.shape}\")\n",
-    "print(f\"lanes: {lanes.shape}\")\n",
-    "print(f\"boundaries: {boundaries.shape}\")\n",
-    "print(f\"traffic: {traffic.shape}\")\n",
-    "\n",
-    "\n",
-    "labels = [\n",
-    "    \"speed\",\n",
-    "    \"width\",\n",
-    "    \"length\",\n",
-    "    \"steering\",\n",
-    "    \"a_long\",\n",
-    "    \"a_lat\",\n",
-    "    \"lane_center_dist_01\",\n",
-    "    \"lane_heading_cos\",\n",
-    "    \"speed_limit\",\n",
-    "]\n",
-    "for name, val in zip(labels, ego):\n",
-    "    print(f\"  {name}: {val:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manual slice verification"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "o = obs[0]  # first agent flat obs\n",
-    "idx = 0\n",
-    "\n",
-    "# Ego\n",
-    "ego_manual = o[idx : idx + env.ego_features]\n",
-    "idx += env.ego_features\n",
-    "assert np.allclose(ego_manual, ego), f\"ego mismatch: {ego_manual} vs {ego}\"\n",
-    "\n",
-    "# Reward conditioning coefs\n",
-    "coefs_manual = o[idx : idx + env.num_reward_coefs]\n",
-    "idx += env.num_reward_coefs\n",
-    "\n",
-    "# Target\n",
-    "target_manual = o[idx : idx + env.num_target_waypoints * env.target_features].reshape(\n",
-    "    env.num_target_waypoints, env.target_features\n",
-    ")\n",
-    "idx += env.num_target_waypoints * env.target_features\n",
-    "assert np.allclose(target_manual, target), \"target mismatch\"\n",
-    "\n",
-    "# Partners\n",
-    "partners_manual = o[idx : idx + env.obs_slots_partners_n * env.partner_features].reshape(\n",
-    "    env.obs_slots_partners_n, env.partner_features\n",
-    ")\n",
-    "idx += env.obs_slots_partners_n * env.partner_features\n",
-    "assert np.allclose(partners_manual, partners), \"partners mismatch\"\n",
-    "\n",
-    "# Lanes\n",
-    "lanes_manual = o[idx : idx + env.obs_slots_lane_kept * env.road_features].reshape(\n",
-    "    env.obs_slots_lane_kept, env.road_features\n",
-    ")\n",
-    "idx += env.obs_slots_lane_kept * env.road_features\n",
-    "assert np.allclose(lanes_manual, lanes), \"lanes mismatch\"\n",
-    "\n",
-    "# Boundaries\n",
-    "bounds_manual = o[idx : idx + env.obs_slots_boundary_kept * env.road_features].reshape(\n",
-    "    env.obs_slots_boundary_kept, env.road_features\n",
-    ")\n",
-    "idx += env.obs_slots_boundary_kept * env.road_features\n",
-    "assert np.allclose(bounds_manual, boundaries), \"boundaries mismatch\"\n",
-    "\n",
-    "# Traffic\n",
-    "traffic_manual = o[idx : idx + env.obs_slots_traffic_controls_n * env.traffic_control_features].reshape(\n",
-    "    env.obs_slots_traffic_controls_n, env.traffic_control_features\n",
-    ")\n",
-    "idx += env.obs_slots_traffic_controls_n * env.traffic_control_features\n",
-    "assert np.allclose(traffic_manual, traffic), \"traffic mismatch\"\n",
-    "\n",
-    idx += 4  # appended slot-count features at end of obs
-    "assert idx == obs.shape[1], f\"obs size mismatch: used {idx}, total {obs.shape[1]}\"\n",
-    "print(f\"All slices match. Total features used: {idx}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reward conditioning coefficients"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coefs = obs[0, env.ego_features : env.ego_features + env.num_reward_coefs]\n",
-    "fig, ax = plt.subplots(figsize=(12, 4))\n",
-    "bars = ax.bar(range(env.num_reward_coefs), coefs, tick_label=COEF_NAMES)\n",
-    "ax.set_ylabel(\"Normalized coef value\")\n",
-    "ax.set_title(\"Reward conditioning coefficients (agent 0)\")\n",
-    "plt.xticks(rotation=45, ha=\"right\")\n",
-    "for bar, val in zip(bars, coefs):\n",
-    "    ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f\"{val:.3f}\", ha=\"center\", va=\"bottom\", fontsize=8)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Compare across agents\n",
-    "all_coefs = obs[:, env.ego_features : env.ego_features + env.num_reward_coefs]\n",
-    "print(\"Coef stats across agents:\")\n",
-    "for i, name in enumerate(COEF_NAMES):\n",
-    "    c = all_coefs[:, i]\n",
-    "    print(f\"  {name:15s}: mean={c.mean():.3f} std={c.std():.3f} min={c.min():.3f} max={c.max():.3f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Partner observations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"heading_cos\", \"heading_sin\", \"rel_vx\", \"rel_vy\"]\n",
-    "active_mask = ~np.all(partners == 0, axis=1)\n",
-    "n_active = active_mask.sum()\n",
-    "print(f\"Active partners: {n_active}/{env.obs_slots_partners_n}\")\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "\n",
-    "# Heatmap\n",
-    "im = axes[0].imshow(partners, aspect=\"auto\", cmap=\"RdBu_r\", vmin=-1, vmax=1)\n",
-    "axes[0].set_xticks(range(env.partner_features))\n",
-    "axes[0].set_xticklabels(partner_labels, rotation=45, ha=\"right\")\n",
-    "axes[0].set_ylabel(\"Partner index\")\n",
-    "axes[0].set_title(f\"Partner obs heatmap ({n_active} active)\")\n",
-    "plt.colorbar(im, ax=axes[0])\n",
-    "\n",
-    "# Scatter in ego frame\n",
-    "active_partners = partners[active_mask]\n",
-    "if len(active_partners) > 0:\n",
-    "    axes[1].scatter(active_partners[:, 0], active_partners[:, 1], c=\"gray\", s=100, edgecolors=\"black\")\n",
-    "    for i, p in enumerate(active_partners):\n",
-    "        axes[1].annotate(str(i), (p[0], p[1]), fontsize=8, ha=\"center\", va=\"bottom\")\n",
-    "axes[1].scatter(0, 0, c=\"blue\", s=200, marker=\"s\", label=\"ego\", zorder=10)\n",
-    "axes[1].set_xlabel(\"rel_x\")\n",
-    "axes[1].set_ylabel(\"rel_y\")\n",
-    "axes[1].set_title(\"Partners in ego frame\")\n",
-    "axes[1].legend()\n",
-    "axes[1].set_aspect(\"equal\")\n",
-    "axes[1].grid(True, alpha=0.3)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lane / boundary segments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "road_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"dir_cos\", \"dir_sin\"]\n",
-    "\n",
-    "lane_active = ~np.all(lanes == 0, axis=1)\n",
-    "bound_active = ~np.all(boundaries == 0, axis=1)\n",
-    "print(\n",
-    "    f\"Active lanes: {lane_active.sum()}/{env.obs_slots_lane_kept}, boundaries: {bound_active.sum()}/{env.obs_slots_boundary_kept}\"\n",
-    ")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "\n",
-    "# Mirror the canonical road rendering in pufferlib.viz.plot_observation\n",
-    "for seg in lanes[lane_active]:\n",
-    "    x, y, z, length, width, dc, ds = seg\n",
-    "    ax.scatter(x, y, color=\"lightgrey\", s=10, zorder=1)\n",
-    "    ax.plot(\n",
-    "        [x + dc * length / 2, x - dc * length / 2],\n",
-    "        [y + ds * length / 2, y - ds * length / 2],\n",
-    "        color=\"lightgrey\",\n",
-    "        linewidth=1,\n",
-    "        zorder=1,\n",
-    "    )\n",
-    "\n",
-    "for seg in boundaries[bound_active]:\n",
-    "    x, y, z, length, width, dc, ds = seg\n",
-    "    ax.scatter(x, y, color=\"black\", s=10, zorder=1)\n",
-    "    ax.plot(\n",
-    "        [x + dc * length / 2, x - dc * length / 2],\n",
-    "        [y + ds * length / 2, y - ds * length / 2],\n",
-    "        color=\"black\",\n",
-    "        linewidth=1,\n",
-    "        zorder=1,\n",
-    "    )\n",
-    "\n",
-    "ax.scatter(0, 0, color=\"blue\", s=200, marker=\"s\", label=\"ego\", zorder=10)\n",
-    "ax.text(\n",
-    "    0.12,\n",
-    "    0.95,\n",
-    "    f\"Lanes: {lane_active.sum()}\\nBoundaries: {bound_active.sum()}\",\n",
-    "    transform=ax.transAxes,\n",
-    "    fontsize=10,\n",
-    "    verticalalignment=\"top\",\n",
-    "    bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.8),\n",
-    ")\n",
-    "ax.axis((-1, 1, -1, 1))\n",
-    "ax.set_aspect(\"equal\", adjustable=\"box\")\n",
-    "ax.set_xlabel(\"X (ego frame)\")\n",
-    "ax.set_ylabel(\"Y (ego frame)\")\n",
-    "ax.set_title(\"Lane + boundary segments in ego frame\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ego-centric view (pufferlib.viz)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img = plot_observation(\n",
-    "    obs[:1],\n",
-    "    target_type=env.target_type,\n",
-    "    reward_conditioning=env.reward_conditioning,\n",
-    "    num_target_waypoints=env.num_target_waypoints,\n",
-    "    obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "    obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "    obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    ")\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "ax.imshow(img)\n",
-    "ax.axis(\"off\")\n",
-    "ax.set_title(\"Ego-centric observation (agent 0)\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bird's eye view (simulator state)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scenarios = env.get_state()\n",
-    "# get_state returns a list of scenario dicts (one per sub-env) or a single dict\n",
-    "if isinstance(scenarios, list):\n",
-    "    scenario = scenarios[0]\n",
-    "else:\n",
-    "    scenario = scenarios\n",
-    "\n",
-    "img = plot_simulator_state(scenario)\n",
-    "fig, ax = plt.subplots(figsize=(12, 12))\n",
-    "ax.imshow(img)\n",
-    "ax.axis(\"off\")\n",
-    "ax.set_title(\"Bird's eye view\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multi-step: ego features over time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_STEPS = 20\n",
-    "ego_history = np.zeros((N_STEPS, env.ego_features))\n",
-    "\n",
-    "for t in range(N_STEPS):\n",
-    "    actions = zero_actions(env)\n",
-    "    obs_t, _, _, _, _ = env.step(actions)\n",
-    "    ego_history[t] = obs_t[0, : env.ego_features]\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(14, 8))\n",
-    "# Speed\n",
-    "axes[0, 0].plot(ego_history[:, 0])\n",
-    "axes[0, 0].set_title(\"speed\")\n",
-    "axes[0, 0].set_xlabel(\"step\")\n",
-    "# Steering\n",
-    "axes[0, 1].plot(ego_history[:, 3])\n",
-    "axes[0, 1].set_title(\"steering\")\n",
-    "axes[0, 1].set_xlabel(\"step\")\n",
-    "# a_long\n",
-    "axes[1, 0].plot(ego_history[:, 4])\n",
-    "axes[1, 0].set_title(\"a_long\")\n",
-    "axes[1, 0].set_xlabel(\"step\")\n",
-    "# a_lat\n",
-    "axes[1, 1].plot(ego_history[:, 5])\n",
-    "axes[1, 1].set_title(\"a_lat\")\n",
-    "axes[1, 1].set_xlabel(\"step\")\n",
-    "plt.suptitle(\"Agent 0 ego features over 20 steps (no-op action)\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cross-agent distributions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Current obs across all agents\n",
-    "# Ego features (jerk): speed(0), width(1), length(2), steering(3), a_long(4), a_lat(5), lane_center(6), lane_heading(7), speed_limit(8)\n",
-    "speeds = obs[:, 0]  # speed is at index 0\n",
-    "\n",
-    "# Target waypoints start after ego + reward coefs\n",
-    "target_start = env.ego_features + env.num_reward_coefs\n",
-    "# Each target waypoint has TARGET_F features; first two are rel_x, rel_y\n",
-    "first_target_x = obs[:, target_start]\n",
-    "first_target_y = obs[:, target_start + 1]\n",
-    "target_dists = np.sqrt(first_target_x**2 + first_target_y**2)\n",
-    "\n",
-    "# Count active partners per agent\n",
-    "partner_start = env.ego_features + env.num_reward_coefs + env.num_target_waypoints * env.target_features\n",
-    "partner_end = partner_start + env.obs_slots_partners_n * env.partner_features\n",
-    "all_partners = obs[:, partner_start:partner_end].reshape(-1, env.obs_slots_partners_n, env.partner_features)\n",
-    "partner_counts = (~np.all(all_partners == 0, axis=2)).sum(axis=1)\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
-    "axes[0].hist(speeds, bins=20, edgecolor=\"black\", alpha=0.7)\n",
-    "axes[0].set_title(f\"Speed distribution (N={len(speeds)})\")\n",
-    "axes[0].set_xlabel(\"speed\")\n",
-    "\n",
-    "axes[1].hist(target_dists, bins=20, edgecolor=\"black\", alpha=0.7, color=\"orange\")\n",
-    "axes[1].set_title(\"Distance to first target waypoint\")\n",
-    "axes[1].set_xlabel(\"distance\")\n",
-    "\n",
-    "axes[2].hist(partner_counts, bins=range(env.obs_slots_partners_n + 2), edgecolor=\"black\", alpha=0.7, color=\"green\")\n",
-    "axes[2].set_title(\"Active partners per agent\")\n",
-    "axes[2].set_xlabel(\"count\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/01_observations.py b/notebooks/01_observations.py
new file mode 100644
index 0000000000..7a60c9825b
--- /dev/null
+++ b/notebooks/01_observations.py
@@ -0,0 +1,375 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.3
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # 01 - Observation Pipeline Debug
+# Verify obs vector is correctly packed, normalized, interpretable.
+
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+from pufferlib.viz import plot_observation, plot_simulator_state, unpack_obs
+from notebooks.notebook_utils import COEF_NAMES, make_drive_env, zero_actions
+
+env, obs, info = make_drive_env()
+
+# %% [markdown]
+# ## Raw obs inspection
+
+# %%
+# Take first step so obs are populated
+actions = zero_actions(env)
+
+obs, rew, term, trunc, info = env.step(actions)
+
+print(f"shape: {obs.shape}, dtype: {obs.dtype}")
+print(f"min: {obs.min():.4f}, max: {obs.max():.4f}, mean: {obs.mean():.4f}, std: {obs.std():.4f}")
+print(f"NaN: {np.isnan(obs).sum()}, Inf: {np.isinf(obs).sum()}")
+print(f"% zeros: {(obs == 0).mean() * 100:.1f}%")
+print(f"% outside [-1,1]: {((obs < -1) | (obs > 1)).mean() * 100:.2f}%")
+
+fig, axes = plt.subplots(1, 2, figsize=(14, 4))
+axes[0].hist(obs.flatten(), bins=100, edgecolor="black", alpha=0.7)
+axes[0].set_title("Full obs distribution")
+axes[0].set_xlabel("Value")
+# Per-agent: show obs[0] vs obs[1]
+for i in range(min(4, obs.shape[0])):
+    axes[1].plot(obs[i], alpha=0.5, label=f"agent {i}")
+axes[1].set_title("Obs vector by index (first 4 agents)")
+axes[1].legend()
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Unpack with pufferlib.viz.unpack_obs
+
+# %%
+ego, target, partners, lanes, boundaries, traffic = unpack_obs(
+    obs[:1],
+    target_type=env.target_type,
+    reward_conditioning=env.reward_conditioning,
+    num_target_waypoints=env.num_target_waypoints,
+    obs_slots_partners_n=env.obs_slots_partners_n,
+    obs_slots_lane_n=env.obs_slots_lane_kept,
+    obs_slots_boundary_n=env.obs_slots_boundary_kept,
+    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+)
+print(f"ego: {ego.shape} = {ego}")
+print(f"target: {target.shape}")
+print(f"partners: {partners.shape}")
+print(f"lanes: {lanes.shape}")
+print(f"boundaries: {boundaries.shape}")
+print(f"traffic: {traffic.shape}")
+
+
+labels = [
+    "speed",
+    "width",
+    "length",
+    "steering",
+    "a_long",
+    "a_lat",
+    "lane_center_dist_01",
+    "lane_heading_cos",
+    "speed_limit",
+]
+for name, val in zip(labels, ego):
+    print(f"  {name}: {val:.4f}")
+
+# %% [markdown]
+# ## Manual slice verification
+
+# %%
+o = obs[0]  # first agent flat obs
+idx = 0
+
+# Ego
+ego_manual = o[idx : idx + env.ego_features]
+idx += env.ego_features
+assert np.allclose(ego_manual, ego), f"ego mismatch: {ego_manual} vs {ego}"
+
+# Reward conditioning coefs
+coefs_manual = o[idx : idx + env.num_reward_coefs]
+idx += env.num_reward_coefs
+
+# Target
+target_manual = o[idx : idx + env.num_target_waypoints * env.target_features].reshape(
+    env.num_target_waypoints, env.target_features
+)
+idx += env.num_target_waypoints * env.target_features
+assert np.allclose(target_manual, target), "target mismatch"
+
+# Partners
+partners_manual = o[idx : idx + env.obs_slots_partners_n * env.partner_features].reshape(
+    env.obs_slots_partners_n, env.partner_features
+)
+idx += env.obs_slots_partners_n * env.partner_features
+assert np.allclose(partners_manual, partners), "partners mismatch"
+
+# Lanes
+lanes_manual = o[idx : idx + env.obs_slots_lane_kept * env.road_features].reshape(
+    env.obs_slots_lane_kept, env.road_features
+)
+idx += env.obs_slots_lane_kept * env.road_features
+assert np.allclose(lanes_manual, lanes), "lanes mismatch"
+
+# Boundaries
+bounds_manual = o[idx : idx + env.obs_slots_boundary_kept * env.road_features].reshape(
+    env.obs_slots_boundary_kept, env.road_features
+)
+idx += env.obs_slots_boundary_kept * env.road_features
+assert np.allclose(bounds_manual, boundaries), "boundaries mismatch"
+
+# Traffic
+traffic_manual = o[idx : idx + env.obs_slots_traffic_controls_n * env.traffic_control_features].reshape(
+    env.obs_slots_traffic_controls_n, env.traffic_control_features
+)
+idx += env.obs_slots_traffic_controls_n * env.traffic_control_features
+assert np.allclose(traffic_manual, traffic), "traffic mismatch"
+
+idx += 4  # appended slot-count features at end of obs
+assert idx == obs.shape[1], f"obs size mismatch: used {idx}, total {obs.shape[1]}"
+print(f"All slices match. Total features used: {idx}")
+
+# %% [markdown]
+# ## Reward conditioning coefficients
+
+# %%
+coefs = obs[0, env.ego_features : env.ego_features + env.num_reward_coefs]
+fig, ax = plt.subplots(figsize=(12, 4))
+bars = ax.bar(range(env.num_reward_coefs), coefs, tick_label=COEF_NAMES)
+ax.set_ylabel("Normalized coef value")
+ax.set_title("Reward conditioning coefficients (agent 0)")
+plt.xticks(rotation=45, ha="right")
+for bar, val in zip(bars, coefs):
+    ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f"{val:.3f}", ha="center", va="bottom", fontsize=8)
+plt.tight_layout()
+plt.show()
+
+# Compare across agents
+all_coefs = obs[:, env.ego_features : env.ego_features + env.num_reward_coefs]
+print("Coef stats across agents:")
+for i, name in enumerate(COEF_NAMES):
+    c = all_coefs[:, i]
+    print(f"  {name:15s}: mean={c.mean():.3f} std={c.std():.3f} min={c.min():.3f} max={c.max():.3f}")
+
+# %% [markdown]
+# ## Partner observations
+
+# %%
+partner_labels = [
+    "rel_x",
+    "rel_y",
+    "rel_z",
+    "length",
+    "width",
+    "heading_cos",
+    "heading_sin",
+    "rel_vx",
+    "rel_vy",
+    "seconds_stopped",
+]
+active_mask = ~np.all(partners == 0, axis=1)
+n_active = active_mask.sum()
+print(f"Active partners: {n_active}/{env.obs_slots_partners_n}")
+
+fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+
+# Heatmap
+im = axes[0].imshow(partners, aspect="auto", cmap="RdBu_r", vmin=-1, vmax=1)
+axes[0].set_xticks(range(env.partner_features))
+axes[0].set_xticklabels(partner_labels, rotation=45, ha="right")
+axes[0].set_ylabel("Partner index")
+axes[0].set_title(f"Partner obs heatmap ({n_active} active)")
+plt.colorbar(im, ax=axes[0])
+
+# Scatter in ego frame
+active_partners = partners[active_mask]
+if len(active_partners) > 0:
+    axes[1].scatter(active_partners[:, 0], active_partners[:, 1], c="gray", s=100, edgecolors="black")
+    for i, p in enumerate(active_partners):
+        axes[1].annotate(str(i), (p[0], p[1]), fontsize=8, ha="center", va="bottom")
+axes[1].scatter(0, 0, c="blue", s=200, marker="s", label="ego", zorder=10)
+axes[1].set_xlabel("rel_x")
+axes[1].set_ylabel("rel_y")
+axes[1].set_title("Partners in ego frame")
+axes[1].legend()
+axes[1].set_aspect("equal")
+axes[1].grid(True, alpha=0.3)
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Lane / boundary segments
+
+# %%
+road_labels = ["rel_x", "rel_y", "rel_z", "length", "width", "dir_cos", "dir_sin"]
+
+lane_active = ~np.all(lanes == 0, axis=1)
+bound_active = ~np.all(boundaries == 0, axis=1)
+print(
+    f"Active lanes: {lane_active.sum()}/{env.obs_slots_lane_kept}, boundaries: {bound_active.sum()}/{env.obs_slots_boundary_kept}"
+)
+
+fig, ax = plt.subplots(figsize=(10, 10))
+
+# Mirror the canonical road rendering in pufferlib.viz.plot_observation
+for seg in lanes[lane_active]:
+    x, y, z, length, width, dc, ds = seg
+    ax.scatter(x, y, color="lightgrey", s=10, zorder=1)
+    ax.plot(
+        [x + dc * length / 2, x - dc * length / 2],
+        [y + ds * length / 2, y - ds * length / 2],
+        color="lightgrey",
+        linewidth=1,
+        zorder=1,
+    )
+
+for seg in boundaries[bound_active]:
+    x, y, z, length, width, dc, ds = seg
+    ax.scatter(x, y, color="black", s=10, zorder=1)
+    ax.plot(
+        [x + dc * length / 2, x - dc * length / 2],
+        [y + ds * length / 2, y - ds * length / 2],
+        color="black",
+        linewidth=1,
+        zorder=1,
+    )
+
+ax.scatter(0, 0, color="blue", s=200, marker="s", label="ego", zorder=10)
+ax.text(
+    0.12,
+    0.95,
+    f"Lanes: {lane_active.sum()}\nBoundaries: {bound_active.sum()}",
+    transform=ax.transAxes,
+    fontsize=10,
+    verticalalignment="top",
+    bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.8),
+)
+ax.axis((-1, 1, -1, 1))
+ax.set_aspect("equal", adjustable="box")
+ax.set_xlabel("X (ego frame)")
+ax.set_ylabel("Y (ego frame)")
+ax.set_title("Lane + boundary segments in ego frame")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Ego-centric view (pufferlib.viz)
+
+# %%
+img = plot_observation(
+    obs[:1],
+    target_type=env.target_type,
+    reward_conditioning=env.reward_conditioning,
+    num_target_waypoints=env.num_target_waypoints,
+    obs_slots_partners_n=env.obs_slots_partners_n,
+    obs_slots_lane_n=env.obs_slots_lane_kept,
+    obs_slots_boundary_n=env.obs_slots_boundary_kept,
+    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+)
+fig, ax = plt.subplots(figsize=(10, 10))
+ax.imshow(img)
+ax.axis("off")
+ax.set_title("Ego-centric observation (agent 0)")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Bird's eye view (simulator state)
+
+# %%
+scenarios = env.get_state()
+# get_state returns a list of scenario dicts (one per sub-env) or a single dict
+if isinstance(scenarios, list):
+    scenario = scenarios[0]
+else:
+    scenario = scenarios
+
+img = plot_simulator_state(scenario)
+fig, ax = plt.subplots(figsize=(12, 12))
+ax.imshow(img)
+ax.axis("off")
+ax.set_title("Bird's eye view")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Multi-step: ego features over time
+
+# %%
+N_STEPS = 20
+ego_history = np.zeros((N_STEPS, env.ego_features))
+
+for t in range(N_STEPS):
+    actions = zero_actions(env)
+    obs_t, _, _, _, _ = env.step(actions)
+    ego_history[t] = obs_t[0, : env.ego_features]
+
+fig, axes = plt.subplots(2, 2, figsize=(14, 8))
+# Speed
+axes[0, 0].plot(ego_history[:, 0])
+axes[0, 0].set_title("speed")
+axes[0, 0].set_xlabel("step")
+# Steering
+axes[0, 1].plot(ego_history[:, 3])
+axes[0, 1].set_title("steering")
+axes[0, 1].set_xlabel("step")
+# a_long
+axes[1, 0].plot(ego_history[:, 4])
+axes[1, 0].set_title("a_long")
+axes[1, 0].set_xlabel("step")
+# a_lat
+axes[1, 1].plot(ego_history[:, 5])
+axes[1, 1].set_title("a_lat")
+axes[1, 1].set_xlabel("step")
+plt.suptitle("Agent 0 ego features over 20 steps (no-op action)")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Cross-agent distributions
+
+# %%
+# Current obs across all agents
+# Ego features (jerk): speed(0), width(1), length(2), steering(3), a_long(4), a_lat(5), lane_center(6), lane_heading(7), speed_limit(8)
+speeds = obs[:, 0]  # speed is at index 0
+
+# Target waypoints start after ego + reward coefs
+target_start = env.ego_features + env.num_reward_coefs
+# Each target waypoint has TARGET_F features; first two are rel_x, rel_y
+first_target_x = obs[:, target_start]
+first_target_y = obs[:, target_start + 1]
+target_dists = np.sqrt(first_target_x**2 + first_target_y**2)
+
+# Count active partners per agent
+partner_start = env.ego_features + env.num_reward_coefs + env.num_target_waypoints * env.target_features
+partner_end = partner_start + env.obs_slots_partners_n * env.partner_features
+all_partners = obs[:, partner_start:partner_end].reshape(-1, env.obs_slots_partners_n, env.partner_features)
+partner_counts = (~np.all(all_partners == 0, axis=2)).sum(axis=1)
+
+fig, axes = plt.subplots(1, 3, figsize=(15, 4))
+axes[0].hist(speeds, bins=20, edgecolor="black", alpha=0.7)
+axes[0].set_title(f"Speed distribution (N={len(speeds)})")
+axes[0].set_xlabel("speed")
+
+axes[1].hist(target_dists, bins=20, edgecolor="black", alpha=0.7, color="orange")
+axes[1].set_title("Distance to first target waypoint")
+axes[1].set_xlabel("distance")
+
+axes[2].hist(partner_counts, bins=range(env.obs_slots_partners_n + 2), edgecolor="black", alpha=0.7, color="green")
+axes[2].set_title("Active partners per agent")
+axes[2].set_xlabel("count")
+plt.tight_layout()
+plt.show()
diff --git a/notebooks/02_rewards.ipynb b/notebooks/02_rewards.ipynb
deleted file mode 100644
index 63372f8f9d..0000000000
--- a/notebooks/02_rewards.ipynb
+++ /dev/null
@@ -1,332 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 02 - Reward Signals Debug\n",
-    "Understand reward magnitudes, components, and correlation with agent behavior."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from notebooks.notebook_utils import COEF_NAMES, make_drive_env, random_actions, zero_actions\n",
-    "\n",
-    "env, obs, info = make_drive_env()\n",
-    "\n",
-    "print(\n",
-    "    f\"env ready: {env.num_agents} agents, obs={obs.shape}, act_shape={(env.num_agents, len(env.single_action_space.nvec))}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"ego_features={env.ego_features}, num_reward_coefs={env.num_reward_coefs}, obs_slots_partners_n={env.obs_slots_partners_n}, partner_features={env.partner_features}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"obs_slots_lane_kept={env.obs_slots_lane_kept}, obs_slots_boundary_kept={env.obs_slots_boundary_kept}, road_features={env.road_features}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"obs_slots_traffic_controls_n={env.obs_slots_traffic_controls_n}, traffic_control_features={env.traffic_control_features}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Single step: no-op reward distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "actions = zero_actions(env)\n",
-    "obs, rew, term, trunc, info = env.step(actions)\n",
-    "\n",
-    "print(f\"reward shape: {rew.shape}\")\n",
-    "print(f\"min: {rew.min():.6f}, max: {rew.max():.6f}, mean: {rew.mean():.6f}, std: {rew.std():.6f}\")\n",
-    "print(f\"NaN: {np.isnan(rew).sum()}, all zero: {(rew == 0).all()}\")\n",
-    "print(f\"terminals: {term.sum()}, truncations: {trunc.sum()}\")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "ax.bar(range(len(rew)), rew, color=[\"red\" if r < 0 else \"green\" for r in rew])\n",
-    "ax.set_xlabel(\"Agent index\")\n",
-    "ax.set_ylabel(\"Reward\")\n",
-    "ax.set_title(\"Single step reward (no-op action)\")\n",
-    "ax.axhline(0, color=\"black\", lw=0.5)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 100-step rollout: reward heatmap and cumulative returns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_STEPS = 100\n",
-    "rewards_history = np.zeros((N_STEPS, env.num_agents))\n",
-    "terms_history = np.zeros((N_STEPS, env.num_agents))\n",
-    "\n",
-    "for t in range(N_STEPS):\n",
-    "    actions = random_actions(env)\n",
-    "    obs, rew, term, trunc, info = env.step(actions)\n",
-    "    rewards_history[t] = rew\n",
-    "    terms_history[t] = term\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
-    "\n",
-    "axes[0].plot(rewards_history.mean(axis=1))\n",
-    "axes[0].set_xlabel(\"Step\")\n",
-    "axes[0].set_ylabel(\"Mean reward\")\n",
-    "axes[0].set_title(\"Mean reward per step\")\n",
-    "\n",
-    "im = axes[1].imshow(rewards_history.T, aspect=\"auto\", cmap=\"RdYlGn\", interpolation=\"nearest\")\n",
-    "axes[1].set_xlabel(\"Step\")\n",
-    "axes[1].set_ylabel(\"Agent\")\n",
-    "axes[1].set_title(\"Reward heatmap (steps x agents)\")\n",
-    "plt.colorbar(im, ax=axes[1])\n",
-    "\n",
-    "cum_returns = rewards_history.cumsum(axis=0)\n",
-    "for i in range(min(8, env.num_agents)):\n",
-    "    axes[2].plot(cum_returns[:, i], alpha=0.6, label=f\"agent {i}\")\n",
-    "axes[2].set_xlabel(\"Step\")\n",
-    "axes[2].set_ylabel(\"Cumulative return\")\n",
-    "axes[2].set_title(\"Cumulative returns\")\n",
-    "axes[2].legend(fontsize=7)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(f\"Total reward stats: mean={rewards_history.mean():.5f}, std={rewards_history.std():.5f}\")\n",
-    "print(f\"Per-episode return (100 steps): mean={cum_returns[-1].mean():.3f}, std={cum_returns[-1].std():.3f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reward coefficient inspection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_coefs = obs[:, env.ego_features : env.ego_features + env.num_reward_coefs]\n",
-    "print(f\"Reward coefs shape: {all_coefs.shape}\")\n",
-    "print()\n",
-    "print(f\"{'Coef':>15s} | {'mean':>8s} {'std':>8s} {'min':>8s} {'max':>8s}\")\n",
-    "print(\"-\" * 55)\n",
-    "for i, name in enumerate(COEF_NAMES):\n",
-    "    c = all_coefs[:, i]\n",
-    "    print(f\"{name:>15s} | {c.mean():8.4f} {c.std():8.4f} {c.min():8.4f} {c.max():8.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Terminal analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_STEPS = 200\n",
-    "term_steps, trunc_steps = [], []\n",
-    "term_rewards, trunc_rewards = [], []\n",
-    "\n",
-    "for t in range(N_STEPS):\n",
-    "    actions = random_actions(env)\n",
-    "    obs, rew, term, trunc, info = env.step(actions)\n",
-    "    for i in range(env.num_agents):\n",
-    "        if term[i]:\n",
-    "            term_steps.append(t)\n",
-    "            term_rewards.append(rew[i])\n",
-    "        if trunc[i]:\n",
-    "            trunc_steps.append(t)\n",
-    "            trunc_rewards.append(rew[i])\n",
-    "\n",
-    "print(f\"Terminals: {len(term_steps)}, Truncations: {len(trunc_steps)}\")\n",
-    "if term_rewards:\n",
-    "    tr = np.array(term_rewards)\n",
-    "    print(f\"Terminal reward: mean={tr.mean():.4f}, std={tr.std():.4f}\")\n",
-    "    n_positive = (tr > 0).sum()\n",
-    "    n_negative = (tr < 0).sum()\n",
-    "    n_zero = (tr == 0).sum()\n",
-    "    print(f\"  positive: {n_positive}, negative: {n_negative}, zero: {n_zero}\")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 4))\n",
-    "if term_steps:\n",
-    "    ax.scatter(term_steps, term_rewards, c=\"red\", s=20, alpha=0.5, label=f\"terminal ({len(term_steps)})\")\n",
-    "if trunc_steps:\n",
-    "    ax.scatter(trunc_steps, trunc_rewards, c=\"blue\", s=20, alpha=0.5, label=f\"truncation ({len(trunc_steps)})\")\n",
-    "ax.axhline(0, color=\"black\", lw=0.5)\n",
-    "ax.set_xlabel(\"Step\")\n",
-    "ax.set_ylabel(\"Reward at terminal/truncation\")\n",
-    "ax.set_title(\"Terminal events over 200 steps\")\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Goal detection: high reward events"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_STEPS = 512\n",
-    "goal_events = []\n",
-    "\n",
-    "for t in range(N_STEPS):\n",
-    "    prev_obs = obs.copy()\n",
-    "    actions = random_actions(env)\n",
-    "    obs, rew, term, trunc, info = env.step(actions)\n",
-    "    for i in range(env.num_agents):\n",
-    "        if rew[i] >= 0.5:\n",
-    "            target_start = env.ego_features + env.num_reward_coefs\n",
-    "            goal_dist = np.sqrt(prev_obs[i, target_start] ** 2 + prev_obs[i, target_start + 1] ** 2)\n",
-    "            goal_events.append((t, i, rew[i], goal_dist))\n",
-    "\n",
-    "print(f\"Goal-like events (reward >= 0.5): {len(goal_events)}\")\n",
-    "if goal_events:\n",
-    "    events = np.array(goal_events)\n",
-    "    print(f\"Reward range: [{events[:, 2].min():.3f}, {events[:, 2].max():.3f}]\")\n",
-    "    print(f\"Goal distance at event: mean={events[:, 3].mean():.3f}, std={events[:, 3].std():.3f}\")\n",
-    "\n",
-    "    fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
-    "    axes[0].hist(events[:, 2], bins=20, edgecolor=\"black\", alpha=0.7, color=\"gold\")\n",
-    "    axes[0].set_title(\"Reward magnitude at goal events\")\n",
-    "    axes[0].set_xlabel(\"Reward\")\n",
-    "    axes[1].scatter(events[:, 3], events[:, 2], alpha=0.5)\n",
-    "    axes[1].set_xlabel(\"Goal distance before event\")\n",
-    "    axes[1].set_ylabel(\"Reward\")\n",
-    "    axes[1].set_title(\"Goal distance vs reward\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "else:\n",
-    "    print(\"No goal events detected in 512 steps with random actions\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reward scale for PPO"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_rewards = rewards_history.flatten()\n",
-    "episodic_returns = rewards_history.sum(axis=0)\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
-    "axes[0].hist(all_rewards[all_rewards != 0], bins=50, edgecolor=\"black\", alpha=0.7)\n",
-    "axes[0].set_title(f\"Per-step reward distribution (non-zero, N={(all_rewards != 0).sum()})\")\n",
-    "axes[0].set_xlabel(\"Reward\")\n",
-    "\n",
-    "axes[1].hist(episodic_returns, bins=20, edgecolor=\"black\", alpha=0.7, color=\"purple\")\n",
-    "axes[1].set_title(f\"Episodic return (100 steps): mean={episodic_returns.mean():.3f}\")\n",
-    "axes[1].set_xlabel(\"Return\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(f\"Reward magnitude range: [{all_rewards.min():.5f}, {all_rewards.max():.5f}]\")\n",
-    "print(f\"Mean episodic return: {episodic_returns.mean():.4f} +/- {episodic_returns.std():.4f}\")\n",
-    "if abs(episodic_returns.mean()) > 10:\n",
-    "    print(\"WARNING: large episodic returns, consider scaling\")\n",
-    "if episodic_returns.std() < 1e-6:\n",
-    "    print(\"WARNING: near-zero return variance\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Action-reward correlation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "STEPS_PER_ACTION = 20\n",
-    "action_rewards = {}\n",
-    "\n",
-    "for a in range(env.single_action_space.nvec[0]):\n",
-    "    rews = []\n",
-    "    for _ in range(STEPS_PER_ACTION):\n",
-    "        actions = np.full((env.num_agents, len(env.single_action_space.nvec)), a, dtype=np.int64)\n",
-    "        obs, rew, term, trunc, info = env.step(actions)\n",
-    "        rews.append(rew.mean())\n",
-    "    action_rewards[a] = np.mean(rews)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 5))\n",
-    "actions_list = sorted(action_rewards.keys())\n",
-    "means = [action_rewards[a] for a in actions_list]\n",
-    "colors = [\"green\" if m > 0 else \"red\" for m in means]\n",
-    "labels = [f\"{a // 3}L,{a % 3}R\" for a in actions_list]\n",
-    "ax.bar(range(len(actions_list)), means, tick_label=labels, color=colors, edgecolor=\"black\")\n",
-    "ax.set_xlabel(\"Action (longitudinal, lateral)\")\n",
-    "ax.set_ylabel(\"Mean reward\")\n",
-    "ax.set_title(f\"Mean reward per action over {STEPS_PER_ACTION} steps\")\n",
-    "ax.axhline(0, color=\"black\", lw=0.5)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/02_rewards.py b/notebooks/02_rewards.py
new file mode 100644
index 0000000000..536f2e0850
--- /dev/null
+++ b/notebooks/02_rewards.py
@@ -0,0 +1,241 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.3
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # 02 - Reward Signals Debug
+# Understand reward magnitudes, components, and correlation with agent behavior.
+
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+from notebooks.notebook_utils import COEF_NAMES, make_drive_env, random_actions, zero_actions
+
+env, obs, info = make_drive_env()
+
+print(
+    f"env ready: {env.num_agents} agents, obs={obs.shape}, act_shape={(env.num_agents, len(env.single_action_space.nvec))}"
+)
+print(
+    f"ego_features={env.ego_features}, num_reward_coefs={env.num_reward_coefs}, obs_slots_partners_n={env.obs_slots_partners_n}, partner_features={env.partner_features}"
+)
+print(
+    f"obs_slots_lane_kept={env.obs_slots_lane_kept}, obs_slots_boundary_kept={env.obs_slots_boundary_kept}, road_features={env.road_features}"
+)
+print(
+    f"obs_slots_traffic_controls_n={env.obs_slots_traffic_controls_n}, traffic_control_features={env.traffic_control_features}"
+)
+
+# %% [markdown]
+# ## Single step: no-op reward distribution
+
+# %%
+actions = zero_actions(env)
+obs, rew, term, trunc, info = env.step(actions)
+
+print(f"reward shape: {rew.shape}")
+print(f"min: {rew.min():.6f}, max: {rew.max():.6f}, mean: {rew.mean():.6f}, std: {rew.std():.6f}")
+print(f"NaN: {np.isnan(rew).sum()}, all zero: {(rew == 0).all()}")
+print(f"terminals: {term.sum()}, truncations: {trunc.sum()}")
+
+fig, ax = plt.subplots(figsize=(8, 4))
+ax.bar(range(len(rew)), rew, color=["red" if r < 0 else "green" for r in rew])
+ax.set_xlabel("Agent index")
+ax.set_ylabel("Reward")
+ax.set_title("Single step reward (no-op action)")
+ax.axhline(0, color="black", lw=0.5)
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## 100-step rollout: reward heatmap and cumulative returns
+
+# %%
+N_STEPS = 100
+rewards_history = np.zeros((N_STEPS, env.num_agents))
+terms_history = np.zeros((N_STEPS, env.num_agents))
+
+for t in range(N_STEPS):
+    actions = random_actions(env)
+    obs, rew, term, trunc, info = env.step(actions)
+    rewards_history[t] = rew
+    terms_history[t] = term
+
+fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+
+axes[0].plot(rewards_history.mean(axis=1))
+axes[0].set_xlabel("Step")
+axes[0].set_ylabel("Mean reward")
+axes[0].set_title("Mean reward per step")
+
+im = axes[1].imshow(rewards_history.T, aspect="auto", cmap="RdYlGn", interpolation="nearest")
+axes[1].set_xlabel("Step")
+axes[1].set_ylabel("Agent")
+axes[1].set_title("Reward heatmap (steps x agents)")
+plt.colorbar(im, ax=axes[1])
+
+cum_returns = rewards_history.cumsum(axis=0)
+for i in range(min(8, env.num_agents)):
+    axes[2].plot(cum_returns[:, i], alpha=0.6, label=f"agent {i}")
+axes[2].set_xlabel("Step")
+axes[2].set_ylabel("Cumulative return")
+axes[2].set_title("Cumulative returns")
+axes[2].legend(fontsize=7)
+plt.tight_layout()
+plt.show()
+
+print(f"Total reward stats: mean={rewards_history.mean():.5f}, std={rewards_history.std():.5f}")
+print(f"Per-episode return (100 steps): mean={cum_returns[-1].mean():.3f}, std={cum_returns[-1].std():.3f}")
+
+# %% [markdown]
+# ## Reward coefficient inspection
+
+# %%
+all_coefs = obs[:, env.ego_features : env.ego_features + env.num_reward_coefs]
+print(f"Reward coefs shape: {all_coefs.shape}")
+print()
+print(f"{'Coef':>15s} | {'mean':>8s} {'std':>8s} {'min':>8s} {'max':>8s}")
+print("-" * 55)
+for i, name in enumerate(COEF_NAMES):
+    c = all_coefs[:, i]
+    print(f"{name:>15s} | {c.mean():8.4f} {c.std():8.4f} {c.min():8.4f} {c.max():8.4f}")
+
+# %% [markdown]
+# ## Terminal analysis
+
+# %%
+N_STEPS = 200
+term_steps, trunc_steps = [], []
+term_rewards, trunc_rewards = [], []
+
+for t in range(N_STEPS):
+    actions = random_actions(env)
+    obs, rew, term, trunc, info = env.step(actions)
+    for i in range(env.num_agents):
+        if term[i]:
+            term_steps.append(t)
+            term_rewards.append(rew[i])
+        if trunc[i]:
+            trunc_steps.append(t)
+            trunc_rewards.append(rew[i])
+
+print(f"Terminals: {len(term_steps)}, Truncations: {len(trunc_steps)}")
+if term_rewards:
+    tr = np.array(term_rewards)
+    print(f"Terminal reward: mean={tr.mean():.4f}, std={tr.std():.4f}")
+    n_positive = (tr > 0).sum()
+    n_negative = (tr < 0).sum()
+    n_zero = (tr == 0).sum()
+    print(f"  positive: {n_positive}, negative: {n_negative}, zero: {n_zero}")
+
+fig, ax = plt.subplots(figsize=(10, 4))
+if term_steps:
+    ax.scatter(term_steps, term_rewards, c="red", s=20, alpha=0.5, label=f"terminal ({len(term_steps)})")
+if trunc_steps:
+    ax.scatter(trunc_steps, trunc_rewards, c="blue", s=20, alpha=0.5, label=f"truncation ({len(trunc_steps)})")
+ax.axhline(0, color="black", lw=0.5)
+ax.set_xlabel("Step")
+ax.set_ylabel("Reward at terminal/truncation")
+ax.set_title("Terminal events over 200 steps")
+ax.legend()
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Goal detection: high reward events
+
+# %%
+N_STEPS = 512
+goal_events = []
+
+for t in range(N_STEPS):
+    prev_obs = obs.copy()
+    actions = random_actions(env)
+    obs, rew, term, trunc, info = env.step(actions)
+    for i in range(env.num_agents):
+        if rew[i] >= 0.5:
+            target_start = env.ego_features + env.num_reward_coefs
+            goal_dist = np.sqrt(prev_obs[i, target_start] ** 2 + prev_obs[i, target_start + 1] ** 2)
+            goal_events.append((t, i, rew[i], goal_dist))
+
+print(f"Goal-like events (reward >= 0.5): {len(goal_events)}")
+if goal_events:
+    events = np.array(goal_events)
+    print(f"Reward range: [{events[:, 2].min():.3f}, {events[:, 2].max():.3f}]")
+    print(f"Goal distance at event: mean={events[:, 3].mean():.3f}, std={events[:, 3].std():.3f}")
+
+    fig, axes = plt.subplots(1, 2, figsize=(12, 4))
+    axes[0].hist(events[:, 2], bins=20, edgecolor="black", alpha=0.7, color="gold")
+    axes[0].set_title("Reward magnitude at goal events")
+    axes[0].set_xlabel("Reward")
+    axes[1].scatter(events[:, 3], events[:, 2], alpha=0.5)
+    axes[1].set_xlabel("Goal distance before event")
+    axes[1].set_ylabel("Reward")
+    axes[1].set_title("Goal distance vs reward")
+    plt.tight_layout()
+    plt.show()
+else:
+    print("No goal events detected in 512 steps with random actions")
+
+# %% [markdown]
+# ## Reward scale for PPO
+
+# %%
+all_rewards = rewards_history.flatten()
+episodic_returns = rewards_history.sum(axis=0)
+
+fig, axes = plt.subplots(1, 2, figsize=(12, 4))
+axes[0].hist(all_rewards[all_rewards != 0], bins=50, edgecolor="black", alpha=0.7)
+axes[0].set_title(f"Per-step reward distribution (non-zero, N={(all_rewards != 0).sum()})")
+axes[0].set_xlabel("Reward")
+
+axes[1].hist(episodic_returns, bins=20, edgecolor="black", alpha=0.7, color="purple")
+axes[1].set_title(f"Episodic return (100 steps): mean={episodic_returns.mean():.3f}")
+axes[1].set_xlabel("Return")
+plt.tight_layout()
+plt.show()
+
+print(f"Reward magnitude range: [{all_rewards.min():.5f}, {all_rewards.max():.5f}]")
+print(f"Mean episodic return: {episodic_returns.mean():.4f} +/- {episodic_returns.std():.4f}")
+if abs(episodic_returns.mean()) > 10:
+    print("WARNING: large episodic returns, consider scaling")
+if episodic_returns.std() < 1e-6:
+    print("WARNING: near-zero return variance")
+
+# %% [markdown]
+# ## Action-reward correlation
+
+# %%
+STEPS_PER_ACTION = 20
+action_rewards = {}
+
+for a in range(env.single_action_space.nvec[0]):
+    rews = []
+    for _ in range(STEPS_PER_ACTION):
+        actions = np.full((env.num_agents, len(env.single_action_space.nvec)), a, dtype=np.int64)
+        obs, rew, term, trunc, info = env.step(actions)
+        rews.append(rew.mean())
+    action_rewards[a] = np.mean(rews)
+
+fig, ax = plt.subplots(figsize=(10, 5))
+actions_list = sorted(action_rewards.keys())
+means = [action_rewards[a] for a in actions_list]
+colors = ["green" if m > 0 else "red" for m in means]
+labels = [f"{a // 3}L,{a % 3}R" for a in actions_list]
+ax.bar(range(len(actions_list)), means, tick_label=labels, color=colors, edgecolor="black")
+ax.set_xlabel("Action (longitudinal, lateral)")
+ax.set_ylabel("Mean reward")
+ax.set_title(f"Mean reward per action over {STEPS_PER_ACTION} steps")
+ax.axhline(0, color="black", lw=0.5)
+plt.tight_layout()
+plt.show()
diff --git a/notebooks/03_metrics.ipynb b/notebooks/03_metrics.ipynb
deleted file mode 100644
index d2e5084a66..0000000000
--- a/notebooks/03_metrics.ipynb
+++ /dev/null
@@ -1,330 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 03 - Episode Metrics & Logging Debug\n",
-    "Verify vec_log returns correct metrics, aggregation is sane, episode boundaries handled."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pufferlib.ocean.drive import binding\n",
-    "from notebooks.notebook_utils import make_drive_env, random_actions\n",
-    "\n",
-    "\n",
-    "env, obs, info = make_drive_env()\n",
-    "\n",
-    "print(\n",
-    "    f\"env ready: {env.num_agents} agents, obs={obs.shape}, act_shape={(env.num_agents, len(env.single_action_space.nvec))}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"ego_features={env.ego_features}, num_reward_coefs={env.num_reward_coefs}, obs_slots_partners_n={env.obs_slots_partners_n}, partner_features={env.partner_features}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"obs_slots_lane_kept={env.obs_slots_lane_kept}, obs_slots_boundary_kept={env.obs_slots_boundary_kept}, road_features={env.road_features}\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"obs_slots_traffic_controls_n={env.obs_slots_traffic_controls_n}, traffic_control_features={env.traffic_control_features}\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Single vec_log call"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _ in range(10):\n",
-    "    actions = random_actions(env)\n",
-    "    obs, rew, term, trunc, info = env.step(actions)\n",
-    "\n",
-    "log = binding.vec_log(env.c_envs, env.num_agents)\n",
-    "print(f\"vec_log type: {type(log)}\")\n",
-    "if log:\n",
-    "    print(f\"Keys: {sorted(log.keys())}\")\n",
-    "    for k, v in sorted(log.items()):\n",
-    "        print(f\"  {k}: {v}\")\n",
-    "else:\n",
-    "    print(\"vec_log returned empty/None\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 512-step collection: all info dicts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_STEPS = 512\n",
-    "all_logs = []\n",
-    "all_rewards = np.zeros((N_STEPS, env.num_agents))\n",
-    "all_terms = np.zeros((N_STEPS, env.num_agents))\n",
-    "all_truncs = np.zeros((N_STEPS, env.num_agents))\n",
-    "\n",
-    "for t in range(N_STEPS):\n",
-    "    actions = random_actions(env)\n",
-    "    obs, rew, term, trunc, info = env.step(actions)\n",
-    "    all_rewards[t] = rew\n",
-    "    all_terms[t] = term\n",
-    "    all_truncs[t] = trunc\n",
-    "    if info:\n",
-    "        for log_entry in info:\n",
-    "            log_entry[\"_step\"] = t\n",
-    "            all_logs.append(log_entry)\n",
-    "\n",
-    "print(f\"Collected {len(all_logs)} log entries over {N_STEPS} steps\")\n",
-    "if all_logs:\n",
-    "    keys = set()\n",
-    "    for log in all_logs:\n",
-    "        keys.update(log.keys())\n",
-    "    keys.discard(\"_step\")\n",
-    "    print(f\"\\n{'Metric':>25s} | {'count':>5s} {'mean':>10s} {'std':>10s} {'min':>10s} {'max':>10s}\")\n",
-    "    print(\"-\" * 75)\n",
-    "    for k in sorted(keys):\n",
-    "        vals = [log[k] for log in all_logs if k in log and isinstance(log[k], (int, float))]\n",
-    "        if vals:\n",
-    "            v = np.array(vals)\n",
-    "            print(f\"{k:>25s} | {len(v):5d} {v.mean():10.4f} {v.std():10.4f} {v.min():10.4f} {v.max():10.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Metric definitions reference\n",
-    "\n",
-    "| Metric | Description |\n",
-    "|--------|-------------|\n",
-    "| score | Goals reached cleanly (no collision/offroad) |\n",
-    "| collision_rate | Fraction of agents that collided |\n",
-    "| offroad_rate | Fraction of agents that went off-road |\n",
-    "| completion_rate | Fraction that reached goal (even with collision/offroad) |\n",
-    "| lane_heading_aligned_rate | Fraction of steps with cos(theta) >= 0.5 (within ~60 deg of lane heading) |\n",
-    "| lane_center_rate | Lane centering metric average (same as reward term) |\n",
-    "| avg_collisions_per_agent | Average collision events per agent per episode |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Terminal / truncation timeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "term_per_step = all_terms.sum(axis=1)\n",
-    "trunc_per_step = all_truncs.sum(axis=1)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(14, 4))\n",
-    "ax.plot(term_per_step, label=\"terminals\", alpha=0.7, color=\"red\")\n",
-    "ax.plot(trunc_per_step, label=\"truncations\", alpha=0.7, color=\"blue\")\n",
-    "ax.set_xlabel(\"Step\")\n",
-    "ax.set_ylabel(\"Count\")\n",
-    "ax.set_title(\"Terminal/truncation events per step\")\n",
-    "ax.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(f\"Total terminals: {all_terms.sum():.0f}, truncations: {all_truncs.sum():.0f}\")\n",
-    "print(f\"Terminals per step: mean={term_per_step.mean():.2f}, max={term_per_step.max():.0f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Agent lifecycle trajectories"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TRACK_STEPS = 100\n",
-    "TRACK_AGENTS = min(5, env.num_agents)\n",
-    "xy_history = np.zeros((TRACK_STEPS, TRACK_AGENTS, 2))\n",
-    "\n",
-    "for t in range(TRACK_STEPS):\n",
-    "    actions = random_actions(env)\n",
-    "    env.step(actions)\n",
-    "    states = env.get_global_agent_state()\n",
-    "    for i in range(TRACK_AGENTS):\n",
-    "        xy_history[t, i, 0] = states[\"x\"][i]\n",
-    "        xy_history[t, i, 1] = states[\"y\"][i]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "for i in range(TRACK_AGENTS):\n",
-    "    ax.plot(xy_history[:, i, 0], xy_history[:, i, 1], \"-o\", markersize=2, alpha=0.7, label=f\"agent {i}\")\n",
-    "    ax.scatter(xy_history[0, i, 0], xy_history[0, i, 1], s=100, marker=\"s\", zorder=10)\n",
-    "    ax.scatter(xy_history[-1, i, 0], xy_history[-1, i, 1], s=100, marker=\"*\", zorder=10)\n",
-    "ax.set_xlabel(\"x\")\n",
-    "ax.set_ylabel(\"y\")\n",
-    "ax.set_title(f\"{TRACK_AGENTS} agent trajectories over {TRACK_STEPS} steps\")\n",
-    "ax.legend()\n",
-    "ax.set_aspect(\"equal\")\n",
-    "ax.grid(True, alpha=0.3)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Consistency checks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if all_logs:\n",
-    "    passed = 0\n",
-    "    failed = 0\n",
-    "    for log in all_logs:\n",
-    "        if \"score\" in log and \"completion_rate\" in log:\n",
-    "            if log[\"score\"] > log[\"completion_rate\"] + 1e-6:\n",
-    "                print(\n",
-    "                    f\"FAIL: score ({log['score']:.4f}) > completion_rate ({log['completion_rate']:.4f}) at step {log['_step']}\"\n",
-    "                )\n",
-    "                failed += 1\n",
-    "            else:\n",
-    "                passed += 1\n",
-    "        for rate_key in [\"collision_rate\", \"offroad_rate\", \"completion_rate\", \"score\"]:\n",
-    "            if rate_key in log:\n",
-    "                v = log[rate_key]\n",
-    "                if v < -1e-6 or v > 1.0 + 1e-6:\n",
-    "                    print(f\"FAIL: {rate_key} = {v:.4f} outside [0,1] at step {log['_step']}\")\n",
-    "                    failed += 1\n",
-    "                else:\n",
-    "                    passed += 1\n",
-    "    print(f\"\\nConsistency checks: {passed} passed, {failed} failed\")\n",
-    "else:\n",
-    "    print(\"No logs to check\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gigaflow agent dynamics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "episode_lengths = []\n",
-    "agent_step_count = np.zeros(env.num_agents)\n",
-    "active_counts = []\n",
-    "\n",
-    "for t in range(N_STEPS):\n",
-    "    active = (~np.all(all_rewards[: t + 1] == 0, axis=0) if t > 0 else np.ones(env.num_agents, dtype=bool)).sum()\n",
-    "    active_counts.append(active)\n",
-    "    for i in range(env.num_agents):\n",
-    "        agent_step_count[i] += 1\n",
-    "        if all_terms[t, i] or all_truncs[t, i]:\n",
-    "            episode_lengths.append(agent_step_count[i])\n",
-    "            agent_step_count[i] = 0\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
-    "axes[0].plot(active_counts)\n",
-    "axes[0].set_xlabel(\"Step\")\n",
-    "axes[0].set_ylabel(\"Active agents\")\n",
-    "axes[0].set_title(\"Active agent count over time\")\n",
-    "\n",
-    "if episode_lengths:\n",
-    "    axes[1].hist(episode_lengths, bins=30, edgecolor=\"black\", alpha=0.7)\n",
-    "    axes[1].set_xlabel(\"Episode length (steps)\")\n",
-    "    axes[1].set_title(f\"Episode length distribution (N={len(episode_lengths)})\")\n",
-    "    print(f\"Episode lengths: mean={np.mean(episode_lengths):.1f}, median={np.median(episode_lengths):.1f}\")\n",
-    "else:\n",
-    "    print(\"No episodes completed\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Score vs cumulative reward"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if all_logs and \"score\" in all_logs[0]:\n",
-    "    scores = [log[\"score\"] for log in all_logs if \"score\" in log]\n",
-    "    log_steps = [log[\"_step\"] for log in all_logs if \"score\" in log]\n",
-    "    cum_rew_at_log = [all_rewards[: t + 1].sum() / env.num_agents for t in log_steps]\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
-    "    ax.scatter(cum_rew_at_log, scores, alpha=0.5)\n",
-    "    ax.set_xlabel(\"Avg cumulative reward up to step\")\n",
-    "    ax.set_ylabel(\"Score\")\n",
-    "    ax.set_title(\"Score vs cumulative reward\")\n",
-    "    ax.grid(True, alpha=0.3)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "else:\n",
-    "    print(\"No score data available\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/03_metrics.py b/notebooks/03_metrics.py
new file mode 100644
index 0000000000..3c04a7dd81
--- /dev/null
+++ b/notebooks/03_metrics.py
@@ -0,0 +1,235 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.3
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # 03 - Episode Metrics & Logging Debug
+# Verify vec_log returns correct metrics, aggregation is sane, episode boundaries handled.
+
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+from pufferlib.ocean.drive import binding
+from notebooks.notebook_utils import make_drive_env, random_actions
+
+
+env, obs, info = make_drive_env()
+
+print(
+    f"env ready: {env.num_agents} agents, obs={obs.shape}, act_shape={(env.num_agents, len(env.single_action_space.nvec))}"
+)
+print(
+    f"ego_features={env.ego_features}, num_reward_coefs={env.num_reward_coefs}, obs_slots_partners_n={env.obs_slots_partners_n}, partner_features={env.partner_features}"
+)
+print(
+    f"obs_slots_lane_kept={env.obs_slots_lane_kept}, obs_slots_boundary_kept={env.obs_slots_boundary_kept}, road_features={env.road_features}"
+)
+print(
+    f"obs_slots_traffic_controls_n={env.obs_slots_traffic_controls_n}, traffic_control_features={env.traffic_control_features}"
+)
+
+# %% [markdown]
+# ## Single vec_log call
+
+# %%
+for _ in range(10):
+    actions = random_actions(env)
+    obs, rew, term, trunc, info = env.step(actions)
+
+log = binding.vec_log(env.c_envs, env.num_agents)
+print(f"vec_log type: {type(log)}")
+if log:
+    print(f"Keys: {sorted(log.keys())}")
+    for k, v in sorted(log.items()):
+        print(f"  {k}: {v}")
+else:
+    print("vec_log returned empty/None")
+
+# %% [markdown]
+# ## 512-step collection: all info dicts
+
+# %%
+N_STEPS = 512
+all_logs = []
+all_rewards = np.zeros((N_STEPS, env.num_agents))
+all_terms = np.zeros((N_STEPS, env.num_agents))
+all_truncs = np.zeros((N_STEPS, env.num_agents))
+
+for t in range(N_STEPS):
+    actions = random_actions(env)
+    obs, rew, term, trunc, info = env.step(actions)
+    all_rewards[t] = rew
+    all_terms[t] = term
+    all_truncs[t] = trunc
+    if info:
+        for log_entry in info:
+            log_entry["_step"] = t
+            all_logs.append(log_entry)
+
+print(f"Collected {len(all_logs)} log entries over {N_STEPS} steps")
+if all_logs:
+    keys = set()
+    for log in all_logs:
+        keys.update(log.keys())
+    keys.discard("_step")
+    print(f"\n{'Metric':>25s} | {'count':>5s} {'mean':>10s} {'std':>10s} {'min':>10s} {'max':>10s}")
+    print("-" * 75)
+    for k in sorted(keys):
+        vals = [log[k] for log in all_logs if k in log and isinstance(log[k], (int, float))]
+        if vals:
+            v = np.array(vals)
+            print(f"{k:>25s} | {len(v):5d} {v.mean():10.4f} {v.std():10.4f} {v.min():10.4f} {v.max():10.4f}")
+
+# %% [markdown]
+# ## Metric definitions reference
+#
+# | Metric | Description |
+# |--------|-------------|
+# | score | Goals reached cleanly (no collision/offroad) |
+# | collision_rate | Fraction of agents that collided |
+# | offroad_rate | Fraction of agents that went off-road |
+# | completion_rate | Fraction that reached goal (even with collision/offroad) |
+# | lane_heading_aligned_rate | Fraction of steps with cos(theta) >= 0.5 (within ~60 deg of lane heading) |
+# | lane_center_rate | Lane centering metric average (same as reward term) |
+# | avg_collisions_per_agent | Average collision events per agent per episode |
+
+# %% [markdown]
+# ## Terminal / truncation timeline
+
+# %%
+term_per_step = all_terms.sum(axis=1)
+trunc_per_step = all_truncs.sum(axis=1)
+
+fig, ax = plt.subplots(figsize=(14, 4))
+ax.plot(term_per_step, label="terminals", alpha=0.7, color="red")
+ax.plot(trunc_per_step, label="truncations", alpha=0.7, color="blue")
+ax.set_xlabel("Step")
+ax.set_ylabel("Count")
+ax.set_title("Terminal/truncation events per step")
+ax.legend()
+plt.tight_layout()
+plt.show()
+
+print(f"Total terminals: {all_terms.sum():.0f}, truncations: {all_truncs.sum():.0f}")
+print(f"Terminals per step: mean={term_per_step.mean():.2f}, max={term_per_step.max():.0f}")
+
+# %% [markdown]
+# ## Agent lifecycle trajectories
+
+# %%
+TRACK_STEPS = 100
+TRACK_AGENTS = min(5, env.num_agents)
+xy_history = np.zeros((TRACK_STEPS, TRACK_AGENTS, 2))
+
+for t in range(TRACK_STEPS):
+    actions = random_actions(env)
+    env.step(actions)
+    states = env.get_global_agent_state()
+    for i in range(TRACK_AGENTS):
+        xy_history[t, i, 0] = states["x"][i]
+        xy_history[t, i, 1] = states["y"][i]
+
+fig, ax = plt.subplots(figsize=(10, 10))
+for i in range(TRACK_AGENTS):
+    ax.plot(xy_history[:, i, 0], xy_history[:, i, 1], "-o", markersize=2, alpha=0.7, label=f"agent {i}")
+    ax.scatter(xy_history[0, i, 0], xy_history[0, i, 1], s=100, marker="s", zorder=10)
+    ax.scatter(xy_history[-1, i, 0], xy_history[-1, i, 1], s=100, marker="*", zorder=10)
+ax.set_xlabel("x")
+ax.set_ylabel("y")
+ax.set_title(f"{TRACK_AGENTS} agent trajectories over {TRACK_STEPS} steps")
+ax.legend()
+ax.set_aspect("equal")
+ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Consistency checks
+
+# %%
+if all_logs:
+    passed = 0
+    failed = 0
+    for log in all_logs:
+        if "score" in log and "completion_rate" in log:
+            if log["score"] > log["completion_rate"] + 1e-6:
+                print(
+                    f"FAIL: score ({log['score']:.4f}) > completion_rate ({log['completion_rate']:.4f}) at step {log['_step']}"
+                )
+                failed += 1
+            else:
+                passed += 1
+        for rate_key in ["collision_rate", "offroad_rate", "completion_rate", "score"]:
+            if rate_key in log:
+                v = log[rate_key]
+                if v < -1e-6 or v > 1.0 + 1e-6:
+                    print(f"FAIL: {rate_key} = {v:.4f} outside [0,1] at step {log['_step']}")
+                    failed += 1
+                else:
+                    passed += 1
+    print(f"\nConsistency checks: {passed} passed, {failed} failed")
+else:
+    print("No logs to check")
+
+# %% [markdown]
+# ## Gigaflow agent dynamics
+
+# %%
+episode_lengths = []
+agent_step_count = np.zeros(env.num_agents)
+active_counts = []
+
+for t in range(N_STEPS):
+    active = (~np.all(all_rewards[: t + 1] == 0, axis=0) if t > 0 else np.ones(env.num_agents, dtype=bool)).sum()
+    active_counts.append(active)
+    for i in range(env.num_agents):
+        agent_step_count[i] += 1
+        if all_terms[t, i] or all_truncs[t, i]:
+            episode_lengths.append(agent_step_count[i])
+            agent_step_count[i] = 0
+
+fig, axes = plt.subplots(1, 2, figsize=(14, 4))
+axes[0].plot(active_counts)
+axes[0].set_xlabel("Step")
+axes[0].set_ylabel("Active agents")
+axes[0].set_title("Active agent count over time")
+
+if episode_lengths:
+    axes[1].hist(episode_lengths, bins=30, edgecolor="black", alpha=0.7)
+    axes[1].set_xlabel("Episode length (steps)")
+    axes[1].set_title(f"Episode length distribution (N={len(episode_lengths)})")
+    print(f"Episode lengths: mean={np.mean(episode_lengths):.1f}, median={np.median(episode_lengths):.1f}")
+else:
+    print("No episodes completed")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Score vs cumulative reward
+
+# %%
+if all_logs and "score" in all_logs[0]:
+    scores = [log["score"] for log in all_logs if "score" in log]
+    log_steps = [log["_step"] for log in all_logs if "score" in log]
+    cum_rew_at_log = [all_rewards[: t + 1].sum() / env.num_agents for t in log_steps]
+
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.scatter(cum_rew_at_log, scores, alpha=0.5)
+    ax.set_xlabel("Avg cumulative reward up to step")
+    ax.set_ylabel("Score")
+    ax.set_title("Score vs cumulative reward")
+    ax.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.show()
+else:
+    print("No score data available")
diff --git a/notebooks/04_training.ipynb b/notebooks/04_training.ipynb
deleted file mode 100644
index b1da07df42..0000000000
--- a/notebooks/04_training.ipynb
+++ /dev/null
@@ -1,549 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 04 - RL Training Loop Debug\n",
-    "End-to-end data flow from env -> policy -> loss. Debug encoding, sampling, advantages, gradients."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from notebooks.notebook_utils import make_drive_env, make_drive_policy, zero_actions\n",
-    "\n",
-    "env, obs, info = make_drive_env()\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "policy = make_drive_policy(env, device)\n",
-    "print(f\"Policy on {device}, params: {sum(p.numel() for p in policy.parameters()):,}\")\n",
-    "print(f\"Action dim: {policy.atn_dim}, act_shape: {(env.num_agents, len(env.single_action_space.nvec))}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optional: load checkpoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# CHECKPOINT_PATH = ''\n",
-    "# state_dict = torch.load(CHECKPOINT_PATH, map_location=device)\n",
-    "# state_dict = {k.replace(\"module.\", \"\"): v for k, v in state_dict.items()}\n",
-    "# print('Checkpoint loaded')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Encode observations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "actions = zero_actions(env)\n",
-    "obs, rew, term, trunc, info = env.step(actions)\n",
-    "\n",
-    "obs_tensor = torch.FloatTensor(obs).to(device)\n",
-    "with torch.no_grad():\n",
-    "    hidden = policy.encode_observations(obs_tensor)\n",
-    "\n",
-    "print(f\"Hidden shape: {hidden.shape}\")\n",
-    "print(f\"Hidden stats: min={hidden.min():.4f}, max={hidden.max():.4f}, mean={hidden.mean():.4f}\")\n",
-    "print(f\"NaN in hidden: {torch.isnan(hidden).sum().item()}\")\n",
-    "print(f\"Dead neurons (always 0): {(hidden.abs().sum(dim=0) == 0).sum().item()}/{hidden.shape[1]}\")\n",
-    "print(f\"% near-zero (<1e-6): {(hidden.abs() < 1e-6).float().mean().item() * 100:.1f}%\")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 4))\n",
-    "ax.hist(hidden.cpu().numpy().flatten(), bins=50, edgecolor=\"black\", alpha=0.7)\n",
-    "ax.set_title(\"Hidden activation distribution\")\n",
-    "ax.set_xlabel(\"Activation value\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Action sampling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with torch.no_grad():\n",
-    "    action_logits, value = policy.decode_actions(hidden)\n",
-    "\n",
-    "for i, logit in enumerate(action_logits):\n",
-    "    print(f\"Action head {i}: shape={logit.shape}\")\n",
-    "    probs = F.softmax(logit, dim=-1)\n",
-    "    entropy = -(probs * probs.log()).sum(dim=-1).mean()\n",
-    "    max_entropy = np.log(logit.shape[-1])\n",
-    "    print(f\"  Entropy: {entropy:.4f} / {max_entropy:.4f} (max) = {entropy / max_entropy:.2%}\")\n",
-    "    print(f\"  Logit range: [{logit.min():.3f}, {logit.max():.3f}]\")\n",
-    "\n",
-    "print(f\"\\nValue: mean={value.mean():.4f}, std={value.std():.4f}\")\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
-    "probs = F.softmax(action_logits[0], dim=-1)\n",
-    "mean_probs = probs.mean(dim=0).cpu().numpy()\n",
-    "axes[0].bar(range(len(mean_probs)), mean_probs, edgecolor=\"black\", alpha=0.7)\n",
-    "axes[0].axhline(1.0 / len(mean_probs), color=\"red\", ls=\"--\", label=\"uniform\")\n",
-    "axes[0].set_xlabel(\"Action\")\n",
-    "axes[0].set_ylabel(\"Probability\")\n",
-    "axes[0].set_title(\"Mean action probabilities\")\n",
-    "axes[0].legend()\n",
-    "\n",
-    "axes[1].hist(value.cpu().numpy().flatten(), bins=20, edgecolor=\"black\", alpha=0.7, color=\"purple\")\n",
-    "axes[1].set_title(\"Value predictions\")\n",
-    "axes[1].set_xlabel(\"Value\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manual encode trace: check each encoder for NaN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = obs_tensor\n",
-    "backbone = policy.actor_backbone\n",
-    "slide_idx = env.ego_features\n",
-    "\n",
-    "ego_obs = x[:, :slide_idx]\n",
-    "print(\n",
-    "    f\"ego_obs: shape={ego_obs.shape}, NaN={torch.isnan(ego_obs).sum().item()}, range=[{ego_obs.min():.3f}, {ego_obs.max():.3f}]\"\n",
-    ")\n",
-    "\n",
-    "cond_dim = backbone.target_dim\n",
-    "if cond_dim > 0:\n",
-    "    cond_obs = x[:, slide_idx : slide_idx + cond_dim]\n",
-    "    slide_idx += cond_dim\n",
-    "    print(f\"cond_obs: shape={cond_obs.shape}, NaN={torch.isnan(cond_obs).sum().item()}\")\n",
-    "\n",
-    "partner_dim = env.obs_slots_partners_n * env.partner_features\n",
-    "lane_dim = env.obs_slots_lane_kept * env.road_features\n",
-    "boundary_dim = env.obs_slots_boundary_kept * env.road_features\n",
-    "\n",
-    "partner_obs = x[:, slide_idx : slide_idx + partner_dim]\n",
-    "slide_idx += partner_dim\n",
-    "lane_obs = x[:, slide_idx : slide_idx + lane_dim]\n",
-    "slide_idx += lane_dim\n",
-    "boundary_obs = x[:, slide_idx : slide_idx + boundary_dim]\n",
-    "slide_idx += boundary_dim\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    ego_enc = backbone.ego_encoder(ego_obs)\n",
-    "    partner_enc, _ = backbone.partner_encoder(partner_obs.view(-1, env.obs_slots_partners_n, env.partner_features)).max(\n",
-    "        dim=1\n",
-    "    )\n",
-    "    lane_enc, _ = backbone.lane_encoder(lane_obs.view(-1, env.obs_slots_lane_kept, env.road_features)).max(dim=1)\n",
-    "    bound_enc, _ = backbone.boundary_encoder(boundary_obs.view(-1, env.obs_slots_boundary_kept, env.road_features)).max(\n",
-    "        dim=1\n",
-    "    )\n",
-    "\n",
-    "for name, enc in [(\"ego\", ego_enc), (\"partner\", partner_enc), (\"lane\", lane_enc), (\"boundary\", bound_enc)]:\n",
-    "    print(\n",
-    "        f\"{name:>10s}_enc: NaN={torch.isnan(enc).sum().item()}, dead={((enc.abs().sum(dim=0) == 0).sum().item())}, range=[{enc.min():.3f}, {enc.max():.3f}]\"\n",
-    "    )\n",
-    "\n",
-    "if cond_dim > 0:\n",
-    "    with torch.no_grad():\n",
-    "        cond_enc = backbone.target_encoder(cond_obs)\n",
-    "    print(\n",
-    "        f\"{'cond':>10s}_enc: NaN={torch.isnan(cond_enc).sum().item()}, dead={((cond_enc.abs().sum(dim=0) == 0).sum().item())}, range=[{cond_enc.min():.3f}, {cond_enc.max():.3f}]\"\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Forward-backward: fake advantage, loss, grads"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "policy.train()\n",
-    "optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4)\n",
-    "\n",
-    "action_logits_list, value = policy(obs_tensor)\n",
-    "\n",
-    "fake_actions = torch.randint(0, env.single_action_space.nvec[0], (env.num_agents,), device=device)\n",
-    "fake_advantages = torch.randn(env.num_agents, device=device)\n",
-    "fake_returns = torch.randn(env.num_agents, device=device)\n",
-    "fake_old_logprobs = torch.randn(env.num_agents, device=device)\n",
-    "\n",
-    "logits = action_logits_list[0]\n",
-    "dist = torch.distributions.Categorical(logits=logits)\n",
-    "new_logprobs = dist.log_prob(fake_actions)\n",
-    "entropy = dist.entropy()\n",
-    "\n",
-    "ratio = torch.exp(new_logprobs - fake_old_logprobs)\n",
-    "clip_coef = 0.2\n",
-    "pg_loss1 = -fake_advantages * ratio\n",
-    "pg_loss2 = -fake_advantages * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)\n",
-    "pg_loss = torch.max(pg_loss1, pg_loss2).mean()\n",
-    "v_loss = 0.5 * ((value.squeeze() - fake_returns) ** 2).mean()\n",
-    "entropy_loss = entropy.mean()\n",
-    "loss = pg_loss + 0.5 * v_loss - 0.01 * entropy_loss\n",
-    "\n",
-    "print(f\"pg_loss: {pg_loss.item():.4f}\")\n",
-    "print(f\"v_loss:  {v_loss.item():.4f}\")\n",
-    "print(f\"entropy: {entropy_loss.item():.4f}\")\n",
-    "print(f\"total:   {loss.item():.4f}\")\n",
-    "print(f\"ratio: mean={ratio.mean():.4f}, std={ratio.std():.4f}\")\n",
-    "\n",
-    "optimizer.zero_grad()\n",
-    "loss.backward()\n",
-    "total_grad_norm = torch.nn.utils.clip_grad_norm_(policy.parameters(), float(\"inf\"))\n",
-    "print(f\"\\nTotal grad norm: {total_grad_norm:.4f}\")\n",
-    "print(f\"NaN in loss: {torch.isnan(loss).item()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Gradient flow: per-parameter analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"{'Parameter':>45s} | {'shape':>20s} | {'grad_norm':>10s} {'grad_mean':>10s} {'grad_max':>10s} | flag\")\n",
-    "print(\"-\" * 120)\n",
-    "for name, param in policy.named_parameters():\n",
-    "    if param.grad is not None:\n",
-    "        g = param.grad\n",
-    "        norm = g.norm().item()\n",
-    "        mean = g.mean().item()\n",
-    "        mx = g.abs().max().item()\n",
-    "        flag = \"\"\n",
-    "        if norm == 0:\n",
-    "            flag = \"ZERO GRAD\"\n",
-    "        elif norm > 100:\n",
-    "            flag = \"EXPLODING\"\n",
-    "        elif norm < 1e-7:\n",
-    "            flag = \"VANISHING\"\n",
-    "        print(f\"{name:>45s} | {str(list(param.shape)):>20s} | {norm:10.6f} {mean:10.6f} {mx:10.6f} | {flag}\")\n",
-    "    else:\n",
-    "        print(f\"{name:>45s} | {str(list(param.shape)):>20s} | NO GRAD\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Experience buffer simulation: 128-step rollout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "HORIZON = 128\n",
-    "obs_dim = obs.shape[1]\n",
-    "\n",
-    "obs_buf = np.zeros((HORIZON, env.num_agents, obs_dim), dtype=np.float32)\n",
-    "act_buf = np.zeros((HORIZON, env.num_agents), dtype=np.int64)\n",
-    "rew_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)\n",
-    "val_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)\n",
-    "logp_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)\n",
-    "done_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)\n",
-    "\n",
-    "policy.eval()\n",
-    "for t in range(HORIZON):\n",
-    "    obs_t = torch.FloatTensor(obs).to(device)\n",
-    "    with torch.no_grad():\n",
-    "        logits_list, val = policy(obs_t)\n",
-    "        dist = torch.distributions.Categorical(logits=logits_list[0])\n",
-    "        act = dist.sample()\n",
-    "        logp = dist.log_prob(act)\n",
-    "\n",
-    "    obs_buf[t] = obs\n",
-    "    act_buf[t] = act.cpu().numpy()\n",
-    "    val_buf[t] = val.squeeze().cpu().numpy()\n",
-    "    logp_buf[t] = logp.cpu().numpy()\n",
-    "\n",
-    "    # Reshape (N,) -> (N, 1) for env.step with MultiDiscrete\n",
-    "    env_actions = act.cpu().numpy().reshape(env.num_agents, len(env.single_action_space.nvec))\n",
-    "    obs, rew, term, trunc, info = env.step(env_actions)\n",
-    "    rew_buf[t] = rew\n",
-    "    done_buf[t] = term | trunc\n",
-    "\n",
-    "print(f\"Buffer shapes: obs={obs_buf.shape}, act={act_buf.shape}, rew={rew_buf.shape}\")\n",
-    "print(f\"Reward stats: mean={rew_buf.mean():.5f}, std={rew_buf.std():.5f}\")\n",
-    "print(f\"Value stats: mean={val_buf.mean():.5f}, std={val_buf.std():.5f}\")\n",
-    "print(f\"Done count: {done_buf.sum():.0f}\")\n",
-    "print(f\"LogProb stats: mean={logp_buf.mean():.4f}, std={logp_buf.std():.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## GAE advantage computation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gamma, lam = 0.98, 0.95\n",
-    "advantages = np.zeros_like(rew_buf)\n",
-    "\n",
-    "last_gae = np.zeros(env.num_agents)\n",
-    "for t in reversed(range(HORIZON - 1)):\n",
-    "    next_non_terminal = 1.0 - done_buf[t + 1]\n",
-    "    delta = rew_buf[t + 1] + gamma * val_buf[t + 1] * next_non_terminal - val_buf[t]\n",
-    "    last_gae = delta + gamma * lam * last_gae * next_non_terminal\n",
-    "    advantages[t] = last_gae\n",
-    "\n",
-    "returns = advantages + val_buf\n",
-    "\n",
-    "print(f\"Advantages: mean={advantages.mean():.5f}, std={advantages.std():.5f}\")\n",
-    "print(f\"Returns: mean={returns.mean():.5f}, std={returns.std():.5f}\")\n",
-    "print(f\"Advantage vs Return corr: {np.corrcoef(advantages.flatten(), returns.flatten())[0, 1]:.4f}\")\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 4, figsize=(18, 4))\n",
-    "axes[0].hist(advantages.flatten(), bins=50, edgecolor=\"black\", alpha=0.7)\n",
-    "axes[0].set_title(f\"Advantage distribution (std={advantages.std():.4f})\")\n",
-    "\n",
-    "axes[1].hist(returns.flatten(), bins=50, edgecolor=\"black\", alpha=0.7, color=\"orange\")\n",
-    "axes[1].set_title(\"Returns distribution\")\n",
-    "\n",
-    "axes[2].plot(advantages.mean(axis=1))\n",
-    "axes[2].set_xlabel(\"Step\")\n",
-    "axes[2].set_ylabel(\"Mean advantage\")\n",
-    "axes[2].set_title(\"Mean advantage over time\")\n",
-    "\n",
-    "axes[3].plot(done_buf.mean(axis=1), color=\"orange\")\n",
-    "axes[3].set_xlabel(\"Step\")\n",
-    "axes[3].set_ylabel(\"Mean done\")\n",
-    "axes[3].set_title(\"Mean done over time\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## PPO loss components"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MB = 16\n",
-    "mb_obs = torch.FloatTensor(obs_buf[:MB].reshape(-1, obs_dim)).to(device)\n",
-    "mb_act = torch.LongTensor(act_buf[:MB].flatten()).to(device)\n",
-    "mb_old_logp = torch.FloatTensor(logp_buf[:MB].flatten()).to(device)\n",
-    "mb_adv = torch.FloatTensor(advantages[:MB].flatten()).to(device)\n",
-    "mb_ret = torch.FloatTensor(returns[:MB].flatten()).to(device)\n",
-    "mb_old_val = torch.FloatTensor(val_buf[:MB].flatten()).to(device)\n",
-    "\n",
-    "mb_adv = (mb_adv - mb_adv.mean()) / (mb_adv.std() + 1e-8)\n",
-    "\n",
-    "policy.train()\n",
-    "logits_list, newvalue = policy(mb_obs)\n",
-    "newvalue = newvalue.squeeze()\n",
-    "dist = torch.distributions.Categorical(logits=logits_list[0])\n",
-    "new_logp = dist.log_prob(mb_act)\n",
-    "entropy = dist.entropy()\n",
-    "\n",
-    "ratio = torch.exp(new_logp - mb_old_logp)\n",
-    "print(f\"Ratio: mean={ratio.mean():.4f}, std={ratio.std():.4f}, min={ratio.min():.4f}, max={ratio.max():.4f}\")\n",
-    "if ratio.mean() < 0.5 or ratio.mean() > 2.0:\n",
-    "    print(\"WARNING: ratio far from 1.0, policy may have diverged\")\n",
-    "\n",
-    "clip_coef = 0.2\n",
-    "pg_loss1 = -mb_adv * ratio\n",
-    "pg_loss2 = -mb_adv * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)\n",
-    "pg_loss = torch.max(pg_loss1, pg_loss2).mean()\n",
-    "\n",
-    "vf_clip = 0.2\n",
-    "v_clipped = mb_old_val + torch.clamp(newvalue - mb_old_val, -vf_clip, vf_clip)\n",
-    "v_loss_unclipped = (newvalue - mb_ret) ** 2\n",
-    "v_loss_clipped = (v_clipped - mb_ret) ** 2\n",
-    "v_loss = 0.5 * torch.max(v_loss_unclipped, v_loss_clipped).mean()\n",
-    "\n",
-    "entropy_loss = entropy.mean()\n",
-    "\n",
-    "print(f\"\\npg_loss:  {pg_loss.item():.6f}\")\n",
-    "print(f\"v_loss:   {v_loss.item():.6f}\")\n",
-    "print(f\"entropy:  {entropy_loss.item():.6f} (max={np.log(env.single_action_space.nvec[0]):.4f})\")\n",
-    "print(f\"total:    {(pg_loss + 0.5 * v_loss - 0.01 * entropy_loss).item():.6f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5-epoch sanity training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4)\n",
-    "all_obs = torch.FloatTensor(obs_buf.reshape(-1, obs_dim)).to(device)\n",
-    "all_act = torch.LongTensor(act_buf.flatten()).to(device)\n",
-    "all_old_logp = torch.FloatTensor(logp_buf.flatten()).to(device)\n",
-    "all_adv = torch.FloatTensor(advantages.flatten()).to(device)\n",
-    "all_ret = torch.FloatTensor(returns.flatten()).to(device)\n",
-    "\n",
-    "all_adv = (all_adv - all_adv.mean()) / (all_adv.std() + 1e-8)\n",
-    "\n",
-    "N_EPOCHS = 5\n",
-    "history = {\"pg_loss\": [], \"v_loss\": [], \"entropy\": [], \"kl\": []}\n",
-    "\n",
-    "policy.train()\n",
-    "for epoch in range(N_EPOCHS):\n",
-    "    logits_list, newval = policy(all_obs)\n",
-    "    newval = newval.squeeze()\n",
-    "    dist = torch.distributions.Categorical(logits=logits_list[0])\n",
-    "    new_logp = dist.log_prob(all_act)\n",
-    "    ent = dist.entropy().mean()\n",
-    "\n",
-    "    ratio = torch.exp(new_logp - all_old_logp)\n",
-    "    approx_kl = (all_old_logp - new_logp).mean()\n",
-    "\n",
-    "    pg1 = -all_adv * ratio\n",
-    "    pg2 = -all_adv * torch.clamp(ratio, 0.8, 1.2)\n",
-    "    pg = torch.max(pg1, pg2).mean()\n",
-    "    vl = 0.5 * ((newval - all_ret) ** 2).mean()\n",
-    "    loss = pg + 0.5 * vl - 0.01 * ent\n",
-    "\n",
-    "    optimizer.zero_grad()\n",
-    "    loss.backward()\n",
-    "    torch.nn.utils.clip_grad_norm_(policy.parameters(), 0.5)\n",
-    "    optimizer.step()\n",
-    "\n",
-    "    history[\"pg_loss\"].append(pg.item())\n",
-    "    history[\"v_loss\"].append(vl.item())\n",
-    "    history[\"entropy\"].append(ent.item())\n",
-    "    history[\"kl\"].append(approx_kl.item())\n",
-    "    print(f\"Epoch {epoch}: pg={pg.item():.5f}, v={vl.item():.5f}, ent={ent.item():.4f}, kl={approx_kl.item():.5f}\")\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 4, figsize=(16, 3))\n",
-    "for i, (key, color) in enumerate(zip(history.keys(), [\"red\", \"blue\", \"green\", \"orange\"])):\n",
-    "    axes[i].plot(history[key], \"-o\", color=color)\n",
-    "    axes[i].set_title(key)\n",
-    "    axes[i].set_xlabel(\"Epoch\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Value accuracy: predicted vs actual returns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "policy.eval()\n",
-    "with torch.no_grad():\n",
-    "    _, pred_values = policy(all_obs)\n",
-    "pred_values = pred_values.squeeze().cpu().numpy()\n",
-    "actual_returns = returns.flatten()\n",
-    "\n",
-    "var_actual = np.var(actual_returns)\n",
-    "explained_var = 1 - np.var(actual_returns - pred_values) / (var_actual + 1e-8) if var_actual > 1e-8 else 0.0\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(7, 7))\n",
-    "ax.scatter(actual_returns, pred_values, alpha=0.3, s=10)\n",
-    "lims = [min(actual_returns.min(), pred_values.min()), max(actual_returns.max(), pred_values.max())]\n",
-    "ax.plot(lims, lims, \"r--\", label=\"perfect\")\n",
-    "ax.set_xlabel(\"Actual return\")\n",
-    "ax.set_ylabel(\"Predicted value\")\n",
-    "ax.set_title(f\"Value accuracy (explained var: {explained_var:.4f})\")\n",
-    "ax.legend()\n",
-    "ax.grid(True, alpha=0.3)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(f\"Explained variance: {explained_var:.4f}\")\n",
-    "print(f\"Value MSE: {np.mean((actual_returns - pred_values) ** 2):.6f}\")\n",
-    "if explained_var < 0:\n",
-    "    print(\"WARNING: negative explained variance, value head worse than predicting mean\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/04_training.py b/notebooks/04_training.py
new file mode 100644
index 0000000000..c1378bd2b1
--- /dev/null
+++ b/notebooks/04_training.py
@@ -0,0 +1,418 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.3
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # 04 - RL Training Loop Debug
+# End-to-end data flow from env -> policy -> loss. Debug encoding, sampling, advantages, gradients.
+
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn.functional as F
+from notebooks.notebook_utils import make_drive_env, make_drive_policy, zero_actions
+
+env, obs, info = make_drive_env()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+policy = make_drive_policy(env, device)
+print(f"Policy on {device}, params: {sum(p.numel() for p in policy.parameters()):,}")
+print(f"Action dim: {policy.atn_dim}, act_shape: {(env.num_agents, len(env.single_action_space.nvec))}")
+
+# %% [markdown]
+# ### Optional: load checkpoint
+
+# %%
+# CHECKPOINT_PATH = ''
+# state_dict = torch.load(CHECKPOINT_PATH, map_location=device)
+# state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
+# print('Checkpoint loaded')
+
+# %% [markdown]
+# ## Encode observations
+
+# %%
+actions = zero_actions(env)
+obs, rew, term, trunc, info = env.step(actions)
+
+obs_tensor = torch.FloatTensor(obs).to(device)
+with torch.no_grad():
+    hidden = policy.encode_observations(obs_tensor)
+
+print(f"Hidden shape: {hidden.shape}")
+print(f"Hidden stats: min={hidden.min():.4f}, max={hidden.max():.4f}, mean={hidden.mean():.4f}")
+print(f"NaN in hidden: {torch.isnan(hidden).sum().item()}")
+print(f"Dead neurons (always 0): {(hidden.abs().sum(dim=0) == 0).sum().item()}/{hidden.shape[1]}")
+print(f"% near-zero (<1e-6): {(hidden.abs() < 1e-6).float().mean().item() * 100:.1f}%")
+
+fig, ax = plt.subplots(figsize=(10, 4))
+ax.hist(hidden.cpu().numpy().flatten(), bins=50, edgecolor="black", alpha=0.7)
+ax.set_title("Hidden activation distribution")
+ax.set_xlabel("Activation value")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Action sampling
+
+# %%
+with torch.no_grad():
+    action_logits, value = policy.decode_actions(hidden)
+
+for i, logit in enumerate(action_logits):
+    print(f"Action head {i}: shape={logit.shape}")
+    probs = F.softmax(logit, dim=-1)
+    entropy = -(probs * probs.log()).sum(dim=-1).mean()
+    max_entropy = np.log(logit.shape[-1])
+    print(f"  Entropy: {entropy:.4f} / {max_entropy:.4f} (max) = {entropy / max_entropy:.2%}")
+    print(f"  Logit range: [{logit.min():.3f}, {logit.max():.3f}]")
+
+print(f"\nValue: mean={value.mean():.4f}, std={value.std():.4f}")
+
+fig, axes = plt.subplots(1, 2, figsize=(14, 4))
+probs = F.softmax(action_logits[0], dim=-1)
+mean_probs = probs.mean(dim=0).cpu().numpy()
+axes[0].bar(range(len(mean_probs)), mean_probs, edgecolor="black", alpha=0.7)
+axes[0].axhline(1.0 / len(mean_probs), color="red", ls="--", label="uniform")
+axes[0].set_xlabel("Action")
+axes[0].set_ylabel("Probability")
+axes[0].set_title("Mean action probabilities")
+axes[0].legend()
+
+axes[1].hist(value.cpu().numpy().flatten(), bins=20, edgecolor="black", alpha=0.7, color="purple")
+axes[1].set_title("Value predictions")
+axes[1].set_xlabel("Value")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Manual encode trace: check each encoder for NaN
+
+# %%
+x = obs_tensor
+backbone = policy.actor_backbone
+slide_idx = env.ego_features
+
+ego_obs = x[:, :slide_idx]
+print(
+    f"ego_obs: shape={ego_obs.shape}, NaN={torch.isnan(ego_obs).sum().item()}, range=[{ego_obs.min():.3f}, {ego_obs.max():.3f}]"
+)
+
+cond_dim = backbone.target_dim
+if cond_dim > 0:
+    cond_obs = x[:, slide_idx : slide_idx + cond_dim]
+    slide_idx += cond_dim
+    print(f"cond_obs: shape={cond_obs.shape}, NaN={torch.isnan(cond_obs).sum().item()}")
+
+partner_dim = env.obs_slots_partners_n * env.partner_features
+lane_dim = env.obs_slots_lane_kept * env.road_features
+boundary_dim = env.obs_slots_boundary_kept * env.road_features
+
+partner_obs = x[:, slide_idx : slide_idx + partner_dim]
+slide_idx += partner_dim
+lane_obs = x[:, slide_idx : slide_idx + lane_dim]
+slide_idx += lane_dim
+boundary_obs = x[:, slide_idx : slide_idx + boundary_dim]
+slide_idx += boundary_dim
+
+with torch.no_grad():
+    ego_enc = backbone.ego_encoder(ego_obs)
+    partner_enc, _ = backbone.partner_encoder(partner_obs.view(-1, env.obs_slots_partners_n, env.partner_features)).max(
+        dim=1
+    )
+    lane_enc, _ = backbone.lane_encoder(lane_obs.view(-1, env.obs_slots_lane_kept, env.road_features)).max(dim=1)
+    bound_enc, _ = backbone.boundary_encoder(boundary_obs.view(-1, env.obs_slots_boundary_kept, env.road_features)).max(
+        dim=1
+    )
+
+for name, enc in [("ego", ego_enc), ("partner", partner_enc), ("lane", lane_enc), ("boundary", bound_enc)]:
+    print(
+        f"{name:>10s}_enc: NaN={torch.isnan(enc).sum().item()}, dead={((enc.abs().sum(dim=0) == 0).sum().item())}, range=[{enc.min():.3f}, {enc.max():.3f}]"
+    )
+
+if cond_dim > 0:
+    with torch.no_grad():
+        cond_enc = backbone.target_encoder(cond_obs)
+    print(
+        f"{'cond':>10s}_enc: NaN={torch.isnan(cond_enc).sum().item()}, dead={((cond_enc.abs().sum(dim=0) == 0).sum().item())}, range=[{cond_enc.min():.3f}, {cond_enc.max():.3f}]"
+    )
+
+# %% [markdown]
+# ## Forward-backward: fake advantage, loss, grads
+
+# %%
+policy.train()
+optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4)
+
+action_logits_list, value = policy(obs_tensor)
+
+fake_actions = torch.randint(0, env.single_action_space.nvec[0], (env.num_agents,), device=device)
+fake_advantages = torch.randn(env.num_agents, device=device)
+fake_returns = torch.randn(env.num_agents, device=device)
+fake_old_logprobs = torch.randn(env.num_agents, device=device)
+
+logits = action_logits_list[0]
+dist = torch.distributions.Categorical(logits=logits)
+new_logprobs = dist.log_prob(fake_actions)
+entropy = dist.entropy()
+
+ratio = torch.exp(new_logprobs - fake_old_logprobs)
+clip_coef = 0.2
+pg_loss1 = -fake_advantages * ratio
+pg_loss2 = -fake_advantages * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)
+pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+v_loss = 0.5 * ((value.squeeze() - fake_returns) ** 2).mean()
+entropy_loss = entropy.mean()
+loss = pg_loss + 0.5 * v_loss - 0.01 * entropy_loss
+
+print(f"pg_loss: {pg_loss.item():.4f}")
+print(f"v_loss:  {v_loss.item():.4f}")
+print(f"entropy: {entropy_loss.item():.4f}")
+print(f"total:   {loss.item():.4f}")
+print(f"ratio: mean={ratio.mean():.4f}, std={ratio.std():.4f}")
+
+optimizer.zero_grad()
+loss.backward()
+total_grad_norm = torch.nn.utils.clip_grad_norm_(policy.parameters(), float("inf"))
+print(f"\nTotal grad norm: {total_grad_norm:.4f}")
+print(f"NaN in loss: {torch.isnan(loss).item()}")
+
+# %% [markdown]
+# ## Gradient flow: per-parameter analysis
+
+# %%
+print(f"{'Parameter':>45s} | {'shape':>20s} | {'grad_norm':>10s} {'grad_mean':>10s} {'grad_max':>10s} | flag")
+print("-" * 120)
+for name, param in policy.named_parameters():
+    if param.grad is not None:
+        g = param.grad
+        norm = g.norm().item()
+        mean = g.mean().item()
+        mx = g.abs().max().item()
+        flag = ""
+        if norm == 0:
+            flag = "ZERO GRAD"
+        elif norm > 100:
+            flag = "EXPLODING"
+        elif norm < 1e-7:
+            flag = "VANISHING"
+        print(f"{name:>45s} | {str(list(param.shape)):>20s} | {norm:10.6f} {mean:10.6f} {mx:10.6f} | {flag}")
+    else:
+        print(f"{name:>45s} | {str(list(param.shape)):>20s} | NO GRAD")
+
+# %% [markdown]
+# ## Experience buffer simulation: 128-step rollout
+
+# %%
+HORIZON = 128
+obs_dim = obs.shape[1]
+
+obs_buf = np.zeros((HORIZON, env.num_agents, obs_dim), dtype=np.float32)
+act_buf = np.zeros((HORIZON, env.num_agents), dtype=np.int64)
+rew_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)
+val_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)
+logp_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)
+done_buf = np.zeros((HORIZON, env.num_agents), dtype=np.float32)
+
+policy.eval()
+for t in range(HORIZON):
+    obs_t = torch.FloatTensor(obs).to(device)
+    with torch.no_grad():
+        logits_list, val = policy(obs_t)
+        dist = torch.distributions.Categorical(logits=logits_list[0])
+        act = dist.sample()
+        logp = dist.log_prob(act)
+
+    obs_buf[t] = obs
+    act_buf[t] = act.cpu().numpy()
+    val_buf[t] = val.squeeze().cpu().numpy()
+    logp_buf[t] = logp.cpu().numpy()
+
+    # Reshape (N,) -> (N, 1) for env.step with MultiDiscrete
+    env_actions = act.cpu().numpy().reshape(env.num_agents, len(env.single_action_space.nvec))
+    obs, rew, term, trunc, info = env.step(env_actions)
+    rew_buf[t] = rew
+    done_buf[t] = term | trunc
+
+print(f"Buffer shapes: obs={obs_buf.shape}, act={act_buf.shape}, rew={rew_buf.shape}")
+print(f"Reward stats: mean={rew_buf.mean():.5f}, std={rew_buf.std():.5f}")
+print(f"Value stats: mean={val_buf.mean():.5f}, std={val_buf.std():.5f}")
+print(f"Done count: {done_buf.sum():.0f}")
+print(f"LogProb stats: mean={logp_buf.mean():.4f}, std={logp_buf.std():.4f}")
+
+# %% [markdown]
+# ## GAE advantage computation
+
+# %%
+gamma, lam = 0.98, 0.95
+advantages = np.zeros_like(rew_buf)
+
+last_gae = np.zeros(env.num_agents)
+for t in reversed(range(HORIZON - 1)):
+    next_non_terminal = 1.0 - done_buf[t + 1]
+    delta = rew_buf[t + 1] + gamma * val_buf[t + 1] * next_non_terminal - val_buf[t]
+    last_gae = delta + gamma * lam * last_gae * next_non_terminal
+    advantages[t] = last_gae
+
+returns = advantages + val_buf
+
+print(f"Advantages: mean={advantages.mean():.5f}, std={advantages.std():.5f}")
+print(f"Returns: mean={returns.mean():.5f}, std={returns.std():.5f}")
+print(f"Advantage vs Return corr: {np.corrcoef(advantages.flatten(), returns.flatten())[0, 1]:.4f}")
+
+fig, axes = plt.subplots(1, 4, figsize=(18, 4))
+axes[0].hist(advantages.flatten(), bins=50, edgecolor="black", alpha=0.7)
+axes[0].set_title(f"Advantage distribution (std={advantages.std():.4f})")
+
+axes[1].hist(returns.flatten(), bins=50, edgecolor="black", alpha=0.7, color="orange")
+axes[1].set_title("Returns distribution")
+
+axes[2].plot(advantages.mean(axis=1))
+axes[2].set_xlabel("Step")
+axes[2].set_ylabel("Mean advantage")
+axes[2].set_title("Mean advantage over time")
+
+axes[3].plot(done_buf.mean(axis=1), color="orange")
+axes[3].set_xlabel("Step")
+axes[3].set_ylabel("Mean done")
+axes[3].set_title("Mean done over time")
+
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## PPO loss components
+
+# %%
+MB = 16
+mb_obs = torch.FloatTensor(obs_buf[:MB].reshape(-1, obs_dim)).to(device)
+mb_act = torch.LongTensor(act_buf[:MB].flatten()).to(device)
+mb_old_logp = torch.FloatTensor(logp_buf[:MB].flatten()).to(device)
+mb_adv = torch.FloatTensor(advantages[:MB].flatten()).to(device)
+mb_ret = torch.FloatTensor(returns[:MB].flatten()).to(device)
+mb_old_val = torch.FloatTensor(val_buf[:MB].flatten()).to(device)
+
+mb_adv = (mb_adv - mb_adv.mean()) / (mb_adv.std() + 1e-8)
+
+policy.train()
+logits_list, newvalue = policy(mb_obs)
+newvalue = newvalue.squeeze()
+dist = torch.distributions.Categorical(logits=logits_list[0])
+new_logp = dist.log_prob(mb_act)
+entropy = dist.entropy()
+
+ratio = torch.exp(new_logp - mb_old_logp)
+print(f"Ratio: mean={ratio.mean():.4f}, std={ratio.std():.4f}, min={ratio.min():.4f}, max={ratio.max():.4f}")
+if ratio.mean() < 0.5 or ratio.mean() > 2.0:
+    print("WARNING: ratio far from 1.0, policy may have diverged")
+
+clip_coef = 0.2
+pg_loss1 = -mb_adv * ratio
+pg_loss2 = -mb_adv * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef)
+pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+vf_clip = 0.2
+v_clipped = mb_old_val + torch.clamp(newvalue - mb_old_val, -vf_clip, vf_clip)
+v_loss_unclipped = (newvalue - mb_ret) ** 2
+v_loss_clipped = (v_clipped - mb_ret) ** 2
+v_loss = 0.5 * torch.max(v_loss_unclipped, v_loss_clipped).mean()
+
+entropy_loss = entropy.mean()
+
+print(f"\npg_loss:  {pg_loss.item():.6f}")
+print(f"v_loss:   {v_loss.item():.6f}")
+print(f"entropy:  {entropy_loss.item():.6f} (max={np.log(env.single_action_space.nvec[0]):.4f})")
+print(f"total:    {(pg_loss + 0.5 * v_loss - 0.01 * entropy_loss).item():.6f}")
+
+# %% [markdown]
+# ## 5-epoch sanity training
+
+# %%
+optimizer = torch.optim.Adam(policy.parameters(), lr=3e-4)
+all_obs = torch.FloatTensor(obs_buf.reshape(-1, obs_dim)).to(device)
+all_act = torch.LongTensor(act_buf.flatten()).to(device)
+all_old_logp = torch.FloatTensor(logp_buf.flatten()).to(device)
+all_adv = torch.FloatTensor(advantages.flatten()).to(device)
+all_ret = torch.FloatTensor(returns.flatten()).to(device)
+
+all_adv = (all_adv - all_adv.mean()) / (all_adv.std() + 1e-8)
+
+N_EPOCHS = 5
+history = {"pg_loss": [], "v_loss": [], "entropy": [], "kl": []}
+
+policy.train()
+for epoch in range(N_EPOCHS):
+    logits_list, newval = policy(all_obs)
+    newval = newval.squeeze()
+    dist = torch.distributions.Categorical(logits=logits_list[0])
+    new_logp = dist.log_prob(all_act)
+    ent = dist.entropy().mean()
+
+    ratio = torch.exp(new_logp - all_old_logp)
+    approx_kl = (all_old_logp - new_logp).mean()
+
+    pg1 = -all_adv * ratio
+    pg2 = -all_adv * torch.clamp(ratio, 0.8, 1.2)
+    pg = torch.max(pg1, pg2).mean()
+    vl = 0.5 * ((newval - all_ret) ** 2).mean()
+    loss = pg + 0.5 * vl - 0.01 * ent
+
+    optimizer.zero_grad()
+    loss.backward()
+    torch.nn.utils.clip_grad_norm_(policy.parameters(), 0.5)
+    optimizer.step()
+
+    history["pg_loss"].append(pg.item())
+    history["v_loss"].append(vl.item())
+    history["entropy"].append(ent.item())
+    history["kl"].append(approx_kl.item())
+    print(f"Epoch {epoch}: pg={pg.item():.5f}, v={vl.item():.5f}, ent={ent.item():.4f}, kl={approx_kl.item():.5f}")
+
+fig, axes = plt.subplots(1, 4, figsize=(16, 3))
+for i, (key, color) in enumerate(zip(history.keys(), ["red", "blue", "green", "orange"])):
+    axes[i].plot(history[key], "-o", color=color)
+    axes[i].set_title(key)
+    axes[i].set_xlabel("Epoch")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Value accuracy: predicted vs actual returns
+
+# %%
+policy.eval()
+with torch.no_grad():
+    _, pred_values = policy(all_obs)
+pred_values = pred_values.squeeze().cpu().numpy()
+actual_returns = returns.flatten()
+
+var_actual = np.var(actual_returns)
+explained_var = 1 - np.var(actual_returns - pred_values) / (var_actual + 1e-8) if var_actual > 1e-8 else 0.0
+
+fig, ax = plt.subplots(figsize=(7, 7))
+ax.scatter(actual_returns, pred_values, alpha=0.3, s=10)
+lims = [min(actual_returns.min(), pred_values.min()), max(actual_returns.max(), pred_values.max())]
+ax.plot(lims, lims, "r--", label="perfect")
+ax.set_xlabel("Actual return")
+ax.set_ylabel("Predicted value")
+ax.set_title(f"Value accuracy (explained var: {explained_var:.4f})")
+ax.legend()
+ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.show()
+
+print(f"Explained variance: {explained_var:.4f}")
+print(f"Value MSE: {np.mean((actual_returns - pred_values) ** 2):.6f}")
+if explained_var < 0:
+    print("WARNING: negative explained variance, value head worse than predicting mean")
diff --git a/notebooks/05_inference.ipynb b/notebooks/05_inference.ipynb
deleted file mode 100644
index c605aaffee..0000000000
--- a/notebooks/05_inference.ipynb
+++ /dev/null
@@ -1,1782 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-0",
-   "metadata": {},
-   "source": [
-    "# 05 - Model Inference Debug\n",
-    "End-to-end inference pipeline: config loading, policy forward pass, rollouts (deterministic vs stochastic), observation/reward analysis, value accuracy, trajectories, LSTM state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from pufferlib.ocean.drive.drive import Drive\n",
-    "from pufferlib.ocean.drive import binding\n",
-    "from pufferlib.ocean.torch import Drive as DrivePolicy\n",
-    "from pufferlib.pytorch import sample_logits\n",
-    "from notebooks.notebook_utils import COEF_NAMES, EGO_LABELS, MAP_DIR, load_notebook_config, zero_actions\n",
-    "\n",
-    "CHECKPOINT_PATH = \"../weights/tomate/models/model_puffer_drive_013100.pt\"\n",
-    "ENV_NAME = \"puffer_drive\"\n",
-    "\n",
-    "config = load_notebook_config(CHECKPOINT_PATH, ENV_NAME)\n",
-    "config[\"env\"][\"num_agents\"] = 64\n",
-    "config[\"env\"][\"num_maps\"] = 8\n",
-    "config[\"env\"][\"eval_mode\"] = 1\n",
-    "config[\"env\"][\"map_dir\"] = MAP_DIR\n",
-    "\n",
-    "config[\"env\"][\"obs_slots_boundary_n\"] = 80\n",
-    "config[\"env\"][\"obs_slots_lane_n\"] = 80\n",
-    "config[\"env\"][\"obs_dropout_lane\"] = 0.0\n",
-    "config[\"env\"][\"obs_dropout_boundary\"] = 0.0\n",
-    "\n",
-    "env = Drive(**config[\"env\"])\n",
-    "obs, info = env.reset(seed=42)\n",
-    "N = env.num_agents\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "policy = DrivePolicy(env, **config[\"policy\"]).to(device)\n",
-    "\n",
-    "if CHECKPOINT_PATH:\n",
-    "    sd = torch.load(CHECKPOINT_PATH, map_location=device)\n",
-    "    sd = {k.replace(\"module.\", \"\"): v for k, v in sd.items()}\n",
-    "    policy.load_state_dict(sd)\n",
-    "    print(f\"Loaded checkpoint: {CHECKPOINT_PATH}\")\n",
-    "\n",
-    "is_continuous = policy.is_continuous\n",
-    "ACT_SHAPE = (N, len(env.single_action_space.nvec)) if not is_continuous else (N, env.single_action_space.shape[0])\n",
-    "\n",
-    "print(f\"Policy on {device}, params: {sum(p.numel() for p in policy.parameters()):,}\")\n",
-    "print(f\"Obs shape: {obs.shape}, Action space: {env.single_action_space}\")\n",
-    "print(f\"Config: dynamics={config['env']['dynamics_model']}, action={config['env']['action_type']}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-2",
-   "metadata": {},
-   "source": [
-    "## Single-step policy output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Take one step to get fresh obs\n",
-    "actions = zero_actions(env)\n",
-    "obs, rew, term, trunc, info = env.step(actions)\n",
-    "\n",
-    "obs_tensor = torch.FloatTensor(obs).to(device)\n",
-    "policy.eval()\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    logits_list, value = policy(obs_tensor)\n",
-    "\n",
-    "# Sample actions\n",
-    "action, logprob, ent = sample_logits(logits_list)\n",
-    "action_det, _, _ = sample_logits(logits_list, deterministic=True)\n",
-    "\n",
-    "print(f\"Value: mean={value.mean():.4f}, std={value.std():.4f}, range=[{value.min():.4f}, {value.max():.4f}]\")\n",
-    "print(f\"Entropy: mean={ent.mean():.4f}, std={ent.std():.4f}\")\n",
-    "print(f\"LogProb: mean={logprob.mean():.4f}, std={logprob.std():.4f}\")\n",
-    "print(f\"Stochastic action sample: {action[0].cpu().numpy()}\")\n",
-    "print(f\"Deterministic action: {action_det[0].cpu().numpy()}\")\n",
-    "\n",
-    "# Plot\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
-    "\n",
-    "# Action probs (first head for multi-discrete, or full logits)\n",
-    "if isinstance(logits_list, list) or isinstance(logits_list, tuple):\n",
-    "    probs = F.softmax(logits_list[0], dim=-1)\n",
-    "else:\n",
-    "    probs = F.softmax(logits_list, dim=-1)\n",
-    "mean_probs = probs.mean(dim=0).cpu().numpy()\n",
-    "axes[0].bar(range(len(mean_probs)), mean_probs, edgecolor=\"black\", alpha=0.7)\n",
-    "axes[0].axhline(1.0 / len(mean_probs), color=\"red\", ls=\"--\", label=\"uniform\")\n",
-    "axes[0].set_xlabel(\"Action\")\n",
-    "axes[0].set_ylabel(\"Probability\")\n",
-    "axes[0].set_title(\"Mean action probabilities (across agents)\")\n",
-    "axes[0].legend()\n",
-    "\n",
-    "axes[1].hist(value.cpu().numpy().flatten(), bins=30, edgecolor=\"black\", alpha=0.7, color=\"purple\")\n",
-    "axes[1].set_title(\"Value predictions across agents\")\n",
-    "axes[1].set_xlabel(\"Value\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-4",
-   "metadata": {},
-   "source": [
-    "## Full rollout: deterministic vs stochastic"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "HORIZON = 256\n",
-    "TRACKED_AGENT = 0  # agent index to track in detail\n",
-    "obs_dim = obs.shape[1]\n",
-    "\n",
-    "dyn_model = config[\"env\"][\"dynamics_model\"]\n",
-    "tgt_type = config[\"env\"][\"target_type\"]\n",
-    "rew_cond = config[\"env\"].get(\"reward_conditioning\", False)\n",
-    "n_tgt_wp = config[\"env\"].get(\"num_target_waypoints\", 3)\n",
-    "\n",
-    "\n",
-    "def run_rollout(env, policy, deterministic=False, horizon=HORIZON):\n",
-    "    obs, _ = env.reset(seed=42)\n",
-    "    N = env.num_agents\n",
-    "\n",
-    "    buffers = {\n",
-    "        \"obs\": np.zeros((horizon, N, obs_dim), dtype=np.float32),\n",
-    "        \"actions\": np.zeros((horizon, N), dtype=np.int64),\n",
-    "        \"rewards\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "        \"values\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "        \"logprobs\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "        \"entropy\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "        \"terminals\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "        \"truncations\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "        \"positions_x\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "        \"positions_y\": np.zeros((horizon, N), dtype=np.float32),\n",
-    "    }\n",
-    "\n",
-    "    policy.eval()\n",
-    "    for t in range(horizon):\n",
-    "        obs_t = torch.FloatTensor(obs).to(device)\n",
-    "        with torch.no_grad():\n",
-    "            logits_list, val = policy(obs_t)\n",
-    "            act, logp, entr = sample_logits(logits_list, deterministic=deterministic)\n",
-    "\n",
-    "        buffers[\"obs\"][t] = obs\n",
-    "        buffers[\"actions\"][t] = act.cpu().numpy().reshape(N) if act.dim() > 1 else act.cpu().numpy()\n",
-    "        buffers[\"values\"][t] = val.squeeze().cpu().numpy()\n",
-    "        buffers[\"logprobs\"][t] = logp.cpu().numpy()\n",
-    "        buffers[\"entropy\"][t] = entr.cpu().numpy()\n",
-    "\n",
-    "        # Get positions\n",
-    "        gstate = env.get_global_agent_state()\n",
-    "        buffers[\"positions_x\"][t] = gstate[\"x\"]\n",
-    "        buffers[\"positions_y\"][t] = gstate[\"y\"]\n",
-    "\n",
-    "        # Step env\n",
-    "        env_actions = act.cpu().numpy().reshape(ACT_SHAPE)\n",
-    "        obs, rew, term, trunc, info = env.step(env_actions)\n",
-    "        buffers[\"rewards\"][t] = rew\n",
-    "        buffers[\"terminals\"][t] = term\n",
-    "        buffers[\"truncations\"][t] = trunc\n",
-    "\n",
-    "    return buffers\n",
-    "\n",
-    "\n",
-    "print(\"Running stochastic rollout...\")\n",
-    "buf_stoch = run_rollout(env, policy, deterministic=False)\n",
-    "print(\"Running deterministic rollout...\")\n",
-    "buf_det = run_rollout(env, policy, deterministic=True)\n",
-    "\n",
-    "for name, buf in [(\"Stochastic\", buf_stoch), (\"Deterministic\", buf_det)]:\n",
-    "    print(f\"\\n--- {name} ---\")\n",
-    "    print(f\"  Reward: mean={buf['rewards'].mean():.5f}, std={buf['rewards'].std():.5f}\")\n",
-    "    print(f\"  Value: mean={buf['values'].mean():.5f}, std={buf['values'].std():.5f}\")\n",
-    "    print(f\"  Entropy: mean={buf['entropy'].mean():.4f}\")\n",
-    "    print(f\"  Terminals: {buf['terminals'].sum():.0f}, Truncations: {buf['truncations'].sum():.0f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-6",
-   "metadata": {},
-   "source": [
-    "## Observation analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pufferlib.viz import unpack_obs, plot_observation, plot_simulator_state\n",
-    "\n",
-    "# Ego-centric observation at t=50 for tracked agent\n",
-    "sample_t = min(50, HORIZON - 1)\n",
-    "sample_obs = buf_stoch[\"obs\"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n",
-    "print(dyn_model, tgt_type, rew_cond, n_tgt_wp)\n",
-    "img = plot_observation(\n",
-    "    sample_obs,\n",
-    "    target_type=tgt_type,\n",
-    "    reward_conditioning=rew_cond,\n",
-    "    num_target_waypoints=n_tgt_wp,\n",
-    "    obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "    obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "    obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    "    obs_norm_goal_offset_m=env.obs_norm_goal_offset_m,\n",
-    "    obs_norm_xy_offset_m=env.obs_norm_xy_offset_m,\n",
-    "    obs_norm_veh_width_m=env.obs_norm_veh_width_m,\n",
-    "    obs_norm_veh_length_m=env.obs_norm_veh_length_m,\n",
-    ")\n",
-    "plt.figure(figsize=(10, 10))\n",
-    "plt.imshow(img)\n",
-    "plt.axis(\"off\")\n",
-    "plt.title(f\"Ego-centric obs | agent={TRACKED_AGENT}, t={sample_t}\")\n",
-    "plt.show()\n",
-    "\n",
-    "# BEV simulator state\n",
-    "scenarios = env.get_state()\n",
-    "if scenarios and len(scenarios) > 0:\n",
-    "    img_bev = plot_simulator_state(scenarios[0], timestep=0)\n",
-    "    plt.figure(figsize=(10, 10))\n",
-    "    plt.imshow(img_bev)\n",
-    "    plt.axis(\"off\")\n",
-    "    plt.title(\"BEV Simulator State\")\n",
-    "    plt.show()\n",
-    "\n",
-    "# Ego feature time series for tracked agent\n",
-    "ego_features_over_time = []\n",
-    "for t in range(HORIZON):\n",
-    "    ego, *_ = unpack_obs(\n",
-    "        buf_stoch[\"obs\"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0],\n",
-    "        target_type=tgt_type,\n",
-    "        reward_conditioning=rew_cond,\n",
-    "        num_target_waypoints=n_tgt_wp,\n",
-    "        obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "        obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "        obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "        obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    "    )\n",
-    "    ego_features_over_time.append(ego)\n",
-    "ego_ts = np.array(ego_features_over_time)\n",
-    "\n",
-    "if dyn_model == \"jerk\":\n",
-    "    labels = [\"speed\", \"width\", \"length\", \"steering\", \"a_long\", \"a_lat\", \"lcenter\", \"lalign\", \"speed_limit\"]\n",
-    "    plot_idxs = [0, 3, 4, 5]  # speed, steering, a_long, a_lat\n",
-    "else:\n",
-    "    labels = [\"speed\", \"width\", \"length\", \"lcenter\", \"lalign\", \"speed_limit\"]\n",
-    "    plot_idxs = [0, 3, 4, 5]  # speed, lcenter, lalign, speed_limit\n",
-    "\n",
-    "fig, axes = plt.subplots(len(plot_idxs), 1, figsize=(14, 3 * len(plot_idxs)), sharex=True)\n",
-    "for i, idx in enumerate(plot_idxs):\n",
-    "    axes[i].plot(ego_ts[:, idx])\n",
-    "    print(ego_ts[10:, idx].argmin())\n",
-    "    axes[i].set_ylabel(labels[idx])\n",
-    "    axes[i].grid(True, alpha=0.3)\n",
-    "axes[-1].set_xlabel(\"Step\")\n",
-    "fig.suptitle(f\"Ego features over time | agent={TRACKED_AGENT}\", fontsize=14)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "czs3aiuhgyo",
-   "metadata": {},
-   "source": [
-    "## Observation layer breakdown\n",
-    "\n",
-    "Obs layout (all ego-centric, normalized):\n",
-    "- **Ego**: speed, width, length, [jerk: steering, a_long, a_lat], lane_center_dist, lane_angle, speed_limit\n",
-    "- **Conditioning** (if enabled): 17 reward coefs (goal_radius, goal_speed, collision, offroad, comfort, lane_align, vel_align, lane_center, center_bias, velocity, reverse, stop_line, timestep, overspeed, throttle, steer, acc) + target waypoints\n",
-    "- **Target**: static=rel_x,rel_y,rel_z per waypoint; dynamic=rel_x,rel_y,rel_z,heading_cos,heading_sin per waypoint\n",
-    "- **Partners** (MAX_PARTNERS x 9): rel_x, rel_y, rel_z, length, width, heading_cos, heading_sin, rel_vx, rel_vy\n",
-    "- **Lanes** (MAX_LANES x 7): rel_x, rel_y, rel_z, seg_length, seg_width, dir_cos, dir_sin\n",
-    "- **Boundaries** (MAX_BOUNDS x 7): same as lanes\n",
-    "- **Traffic controls** (MAX_TRAFFIC x 7): rel_x1, rel_y1, rel_x2, rel_y2, rel_z, type, state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ytjdjnb471l",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pufferlib.viz import unpack_obs\n",
-    "\n",
-    "sample_t = min(50, HORIZON - 1)\n",
-    "sample_obs = buf_stoch[\"obs\"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n",
-    "ego, target, partners, lanes, boundaries, traffic_controls = unpack_obs(\n",
-    "    sample_obs,\n",
-    "    target_type=tgt_type,\n",
-    "    reward_conditioning=rew_cond,\n",
-    "    num_target_waypoints=n_tgt_wp,\n",
-    "    obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "    obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "    obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    ")\n",
-    "\n",
-    "# Also unpack conditioning manually (unpack_obs doesn't return it separately)\n",
-    "ego_dim = binding.EGO_FEATURES\n",
-    "cond_dim = binding.NUM_REWARD_COEFS if rew_cond else 0\n",
-    "cond_obs = sample_obs[0, ego_dim : ego_dim + cond_dim] if cond_dim > 0 else None\n",
-    "\n",
-    "\n",
-    "# --- Print all layer shapes + stats ---\n",
-    "def layer_stats(name, arr):\n",
-    "    flat = arr.flatten() if hasattr(arr, \"flatten\") else np.array(arr).flatten()\n",
-    "    if flat.size == 0:\n",
-    "        print(f\"{name:>14s}: shape={str(list(arr.shape)):>16s}  (empty)\")\n",
-    "        return\n",
-    "    nonzero = np.count_nonzero(flat)\n",
-    "    print(\n",
-    "        f\"{name:>14s}: shape={str(list(arr.shape)):>16s}  \"\n",
-    "        f\"nonzero={nonzero:>5d}/{flat.size:<5d}  \"\n",
-    "        f\"range=[{flat.min():.4f}, {flat.max():.4f}]  \"\n",
-    "        f\"mean={flat.mean():.4f}  std={flat.std():.4f}\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "print(f\"--- Observation breakdown at t={sample_t}, agent={TRACKED_AGENT} ---\")\n",
-    "print(f\"Total obs dim: {sample_obs.shape[-1]}\")\n",
-    "print()\n",
-    "layer_stats(\"Ego\", ego)\n",
-    "if cond_obs is not None:\n",
-    "    layer_stats(\"Conditioning\", cond_obs)\n",
-    "layer_stats(\"Target\", target)\n",
-    "layer_stats(\"Partners\", partners)\n",
-    "layer_stats(\"Lanes\", lanes)\n",
-    "layer_stats(\"Boundaries\", boundaries)\n",
-    "layer_stats(\"TrafficControls\", traffic_controls)\n",
-    "\n",
-    "# --- Ego features detail ---\n",
-    "ego_labels = EGO_LABELS\n",
-    "\n",
-    "print(f\"\\n--- Ego features ---\")\n",
-    "for i, (label, val) in enumerate(zip(ego_labels, ego)):\n",
-    "    print(f\"  [{i}] {label:>14s} = {val:.4f}\")\n",
-    "\n",
-    "# --- Conditioning detail ---\n",
-    "if cond_obs is not None:\n",
-    "    cond_labels = COEF_NAMES\n",
-    "    print(f\"\\n--- Conditioning (reward coefs, normalized) ---\")\n",
-    "    for i, (label, val) in enumerate(zip(cond_labels, cond_obs)):\n",
-    "        print(f\"  [{i:>2d}] {label:>16s} = {val:.4f}\")\n",
-    "\n",
-    "# --- Target waypoints ---\n",
-    "tgt_feat = binding.STATIC_TARGET_FEATURES if tgt_type == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n",
-    "if tgt_type == \"static\":\n",
-    "    tgt_labels = [\"rel_x\", \"rel_y\", \"rel_z\"]\n",
-    "else:\n",
-    "    tgt_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"heading_cos\", \"heading_sin\"]\n",
-    "\n",
-    "print(f\"\\n--- Target waypoints (n={n_tgt_wp}, type={tgt_type}) ---\")\n",
-    "for wp in range(target.shape[0]):\n",
-    "    vals = \", \".join(f\"{tgt_labels[j]}={target[wp, j]:.4f}\" for j in range(tgt_feat))\n",
-    "    active = \"ACTIVE\" if not np.allclose(target[wp], 0) else \"zeroed\"\n",
-    "    print(f\"  wp[{wp}]: {vals}  ({active})\")\n",
-    "\n",
-    "# --- Partner summary ---\n",
-    "n_visible = np.sum(np.any(partners != 0, axis=1))\n",
-    "print(f\"\\n--- Partners: {n_visible}/{partners.shape[0]} visible ---\")\n",
-    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"heading_cos\", \"heading_sin\", \"rel_vx\", \"rel_vy\"]\n",
-    "for p in range(min(int(n_visible), 5)):\n",
-    "    vals = \", \".join(f\"{partner_labels[j]}={partners[p, j]:.3f}\" for j in range(env.partner_features))\n",
-    "    print(f\"  [{p}] {vals}\")\n",
-    "if n_visible > 5:\n",
-    "    print(f\"  ... ({n_visible - 5} more)\")\n",
-    "\n",
-    "# --- Lane/boundary occupancy ---\n",
-    "n_lanes = np.sum(np.any(lanes != 0, axis=1))\n",
-    "n_bounds = np.sum(np.any(boundaries != 0, axis=1))\n",
-    "print(f\"\\n--- Road: {n_lanes}/{lanes.shape[0]} lane segs, {n_bounds}/{boundaries.shape[0]} boundary segs ---\")\n",
-    "\n",
-    "# --- Traffic ---\n",
-    "n_traffic = np.sum(np.any(traffic_controls != 0, axis=1))\n",
-    "print(f\"\\n--- Traffic controls: {n_traffic}/{traffic_controls.shape[0]} visible ---\")\n",
-    "traffic_labels = [\"rel_x1\", \"rel_y1\", \"rel_x2\", \"rel_y2\", \"rel_z\", \"type\", \"state\"]\n",
-    "for t in range(min(int(n_traffic), 5)):\n",
-    "    vals = \", \".join(\n",
-    "        f\"{traffic_labels[j]}={traffic_controls[t, j]:.3f}\"\n",
-    "        for j in range(min(len(traffic_labels), traffic_controls.shape[1]))\n",
-    "    )\n",
-    "    print(f\"  [{t}] {vals}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65bg95pn7dp",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# --- Layer-level stats across ALL agents at sample_t ---\n",
-    "all_obs = buf_stoch[\"obs\"][sample_t]  # (N, obs_dim)\n",
-    "\n",
-    "ego_dim = binding.EGO_FEATURES\n",
-    "cond_dim = binding.NUM_REWARD_COEFS if rew_cond else 0\n",
-    "tgt_feat = binding.STATIC_TARGET_FEATURES if tgt_type == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n",
-    "tgt_dim = n_tgt_wp * tgt_feat\n",
-    "partner_dim = env.obs_slots_partners_n * env.partner_features\n",
-    "lane_dim = env.obs_slots_lane_kept * env.road_features\n",
-    "boundary_dim = env.obs_slots_boundary_kept * env.road_features\n",
-    "traffic_dim = env.obs_slots_traffic_controls_n * env.traffic_control_features\n",
-    "\n",
-    "# Slice indices\n",
-    "idx = 0\n",
-    "slices = {}\n",
-    "slices[\"ego\"] = (idx, idx + ego_dim)\n",
-    "idx += ego_dim\n",
-    "if cond_dim > 0:\n",
-    "    slices[\"conditioning\"] = (idx, idx + cond_dim)\n",
-    "    idx += cond_dim\n",
-    "slices[\"target\"] = (idx, idx + tgt_dim)\n",
-    "idx += tgt_dim\n",
-    "slices[\"partners\"] = (idx, idx + partner_dim)\n",
-    "idx += partner_dim\n",
-    "slices[\"lanes\"] = (idx, idx + lane_dim)\n",
-    "idx += lane_dim\n",
-    "slices[\"boundaries\"] = (idx, idx + boundary_dim)\n",
-    "idx += boundary_dim\n",
-    "slices[\"traffic\"] = (idx, idx + traffic_dim)\n",
-    "idx += traffic_dim\n",
-    "\n",
-    "print(f\"Obs dim used: {idx} / {all_obs.shape[1]}\")\n",
-    "print(\n",
-    "    f\"\\n{'Layer':>14s} | {'start':>5s}-{'end':>5s} | {'dim':>5s} | {'mean':>8s} | {'std':>8s} | {'min':>8s} | {'max':>8s} | {'%nonzero':>8s}\"\n",
-    ")\n",
-    "print(\"-\" * 95)\n",
-    "for name, (s, e) in slices.items():\n",
-    "    chunk = all_obs[:, s:e]\n",
-    "    nz_pct = 100 * np.count_nonzero(chunk) / chunk.size\n",
-    "    print(\n",
-    "        f\"{name:>14s} | {s:>5d}-{e:>5d} | {e - s:>5d} | {chunk.mean():>8.4f} | {chunk.std():>8.4f} | \"\n",
-    "        f\"{chunk.min():>8.4f} | {chunk.max():>8.4f} | {nz_pct:>7.1f}%\"\n",
-    "    )\n",
-    "\n",
-    "# --- Plots ---\n",
-    "n_layers = len(slices)\n",
-    "fig, axes = plt.subplots(2, (n_layers + 1) // 2, figsize=(5 * ((n_layers + 1) // 2), 8))\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "for i, (name, (s, e)) in enumerate(slices.items()):\n",
-    "    chunk = all_obs[:, s:e].flatten()\n",
-    "    # Filter out exact zeros for histogram readability on sparse layers\n",
-    "    nonzero_vals = chunk[chunk != 0]\n",
-    "    if len(nonzero_vals) > 0:\n",
-    "        axes[i].hist(nonzero_vals, bins=50, edgecolor=\"black\", alpha=0.7)\n",
-    "        axes[i].set_title(f\"{name} (nonzero only, {len(nonzero_vals)}/{len(chunk)})\")\n",
-    "    else:\n",
-    "        axes[i].hist(chunk, bins=50, edgecolor=\"black\", alpha=0.7)\n",
-    "        axes[i].set_title(f\"{name} (all zeros)\")\n",
-    "    axes[i].set_xlabel(\"Value\")\n",
-    "\n",
-    "# Hide unused axes\n",
-    "for j in range(i + 1, len(axes)):\n",
-    "    axes[j].set_visible(False)\n",
-    "\n",
-    "fig.suptitle(f\"Observation distributions across {N} agents at t={sample_t}\", fontsize=14)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dfm7par7pmo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# --- Per-feature detail for partners, lanes, boundaries over time (tracked agent) ---\n",
-    "\n",
-    "\n",
-    "def unpack_all_timesteps(bufs, agent_idx):\n",
-    "    \"\"\"Unpack all obs layers across time for one agent.\"\"\"\n",
-    "    H = bufs[\"obs\"].shape[0]\n",
-    "    egos, targets, conds = [], [], []\n",
-    "    n_partners, n_lanes, n_bounds, n_traffic = [], [], [], []\n",
-    "\n",
-    "    for t in range(H):\n",
-    "        ob = bufs[\"obs\"][t : t + 1, agent_idx : agent_idx + 1][0]\n",
-    "        ego, tgt, part, lane, bnd, tfc = unpack_obs(\n",
-    "            ob,\n",
-    "            target_type=tgt_type,\n",
-    "            reward_conditioning=rew_cond,\n",
-    "            num_target_waypoints=n_tgt_wp,\n",
-    "            obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "            obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "            obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "            obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    "        )\n",
-    "        egos.append(ego)\n",
-    "        targets.append(tgt)\n",
-    "        n_partners.append(np.sum(np.any(part != 0, axis=1)))\n",
-    "        n_lanes.append(np.sum(np.any(lane != 0, axis=1)))\n",
-    "        n_bounds.append(np.sum(np.any(bnd != 0, axis=1)))\n",
-    "        n_traffic.append(np.sum(np.any(tfc != 0, axis=1)))\n",
-    "\n",
-    "        if rew_cond:\n",
-    "            ed = binding.EGO_FEATURES\n",
-    "            conds.append(ob[0, ed : ed + binding.NUM_REWARD_COEFS])\n",
-    "\n",
-    "    return {\n",
-    "        \"ego\": np.array(egos),\n",
-    "        \"target\": np.array(targets),\n",
-    "        \"cond\": np.array(conds) if conds else None,\n",
-    "        \"n_partners\": np.array(n_partners),\n",
-    "        \"n_lanes\": np.array(n_lanes),\n",
-    "        \"n_bounds\": np.array(n_bounds),\n",
-    "        \"n_traffic\": np.array(n_traffic),\n",
-    "    }\n",
-    "\n",
-    "\n",
-    "ts = unpack_all_timesteps(buf_stoch, TRACKED_AGENT)\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n",
-    "\n",
-    "# Occupancy over time\n",
-    "axes[0, 0].plot(ts[\"n_partners\"], label=\"partners\", alpha=0.8)\n",
-    "axes[0, 0].plot(ts[\"n_lanes\"], label=\"lanes\", alpha=0.8)\n",
-    "axes[0, 0].plot(ts[\"n_bounds\"], label=\"boundaries\", alpha=0.8)\n",
-    "axes[0, 0].plot(ts[\"n_traffic\"], label=\"traffic\", alpha=0.8)\n",
-    "axes[0, 0].set_xlabel(\"Step\")\n",
-    "axes[0, 0].set_ylabel(\"Visible count\")\n",
-    "axes[0, 0].set_title(f\"Obs occupancy over time | agent={TRACKED_AGENT}\")\n",
-    "axes[0, 0].legend()\n",
-    "axes[0, 0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Target waypoint distances over time\n",
-    "tgt_x = ts[\"target\"][:, :, 0]\n",
-    "tgt_y = ts[\"target\"][:, :, 1]\n",
-    "tgt_dist = np.sqrt(tgt_x**2 + tgt_y**2)\n",
-    "for wp in range(n_tgt_wp):\n",
-    "    axes[0, 1].plot(tgt_dist[:, wp], label=f\"wp[{wp}]\", alpha=0.8)\n",
-    "axes[0, 1].set_xlabel(\"Step\")\n",
-    "axes[0, 1].set_ylabel(\"Distance (normalized)\")\n",
-    "axes[0, 1].set_title(\"Target waypoint distance over time\")\n",
-    "axes[0, 1].legend()\n",
-    "axes[0, 1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Conditioning heatmap over time\n",
-    "if ts[\"cond\"] is not None:\n",
-    "    cond_labels = COEF_NAMES\n",
-    "    im = axes[1, 0].imshow(ts[\"cond\"].T, aspect=\"auto\", cmap=\"coolwarm\", interpolation=\"nearest\")\n",
-    "    axes[1, 0].set_yticks(range(len(cond_labels)))\n",
-    "    axes[1, 0].set_yticklabels(cond_labels, fontsize=8)\n",
-    "    axes[1, 0].set_xlabel(\"Step\")\n",
-    "    axes[1, 0].set_title(\"Conditioning coefs over time\")\n",
-    "    plt.colorbar(im, ax=axes[1, 0])\n",
-    "else:\n",
-    "    axes[1, 0].text(0.5, 0.5, \"No conditioning\", ha=\"center\", va=\"center\", transform=axes[1, 0].transAxes)\n",
-    "    axes[1, 0].set_title(\"Conditioning (disabled)\")\n",
-    "\n",
-    "# Partner closest distance over time\n",
-    "partner_dists = []\n",
-    "for t in range(HORIZON):\n",
-    "    ob = buf_stoch[\"obs\"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n",
-    "    _, _, part, _, _, _ = unpack_obs(\n",
-    "        ob,\n",
-    "        target_type=tgt_type,\n",
-    "        reward_conditioning=rew_cond,\n",
-    "        num_target_waypoints=n_tgt_wp,\n",
-    "        obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "        obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "        obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "        obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    "    )\n",
-    "    dists = np.sqrt(part[:, 0] ** 2 + part[:, 1] ** 2)\n",
-    "    visible = np.any(part != 0, axis=1)\n",
-    "    partner_dists.append(dists[visible].min() if visible.any() else np.nan)\n",
-    "\n",
-    "axes[1, 1].plot(partner_dists, alpha=0.8, color=\"red\")\n",
-    "axes[1, 1].set_xlabel(\"Step\")\n",
-    "axes[1, 1].set_ylabel(\"Min partner dist (normalized)\")\n",
-    "axes[1, 1].set_title(\"Closest partner distance over time\")\n",
-    "axes[1, 1].grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "j1h4h99tnve",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# --- Spatial scatter: all observed entities in ego frame at sample_t ---\n",
-    "sample_obs = buf_stoch[\"obs\"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]\n",
-    "ego, target, partners, lanes, boundaries, traffic_controls = unpack_obs(\n",
-    "    sample_obs,\n",
-    "    target_type=tgt_type,\n",
-    "    reward_conditioning=rew_cond,\n",
-    "    num_target_waypoints=n_tgt_wp,\n",
-    "    obs_slots_partners_n=env.obs_slots_partners_n,\n",
-    "    obs_slots_lane_n=env.obs_slots_lane_kept,\n",
-    "    obs_slots_boundary_n=env.obs_slots_boundary_kept,\n",
-    "    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,\n",
-    ")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
-    "\n",
-    "# Ego vehicle at origin\n",
-    "from matplotlib.patches import Rectangle\n",
-    "\n",
-    "ax.add_patch(\n",
-    "    Rectangle((-ego[2] / 2, -ego[1] / 2), ego[2], ego[1], facecolor=\"blue\", edgecolor=\"black\", alpha=0.7, zorder=10)\n",
-    ")\n",
-    "ax.annotate(\"EGO\", (0, 0), fontsize=9, ha=\"center\", va=\"center\", color=\"white\", fontweight=\"bold\", zorder=11)\n",
-    "\n",
-    "# Lane segments\n",
-    "for i in range(lanes.shape[0]):\n",
-    "    if np.allclose(lanes[i], 0):\n",
-    "        continue\n",
-    "    rx, ry, rz, length, _, dc, ds = lanes[i]\n",
-    "    ax.plot(\n",
-    "        [rx - dc * length / 2, rx + dc * length / 2],\n",
-    "        [ry - ds * length / 2, ry + ds * length / 2],\n",
-    "        color=\"lightgray\",\n",
-    "        linewidth=1,\n",
-    "        zorder=1,\n",
-    "    )\n",
-    "ax.scatter(\n",
-    "    lanes[np.any(lanes != 0, axis=1), 0],\n",
-    "    lanes[np.any(lanes != 0, axis=1), 1],\n",
-    "    s=5,\n",
-    "    color=\"gray\",\n",
-    "    alpha=0.5,\n",
-    "    label=f\"lanes ({n_lanes})\",\n",
-    "    zorder=2,\n",
-    ")\n",
-    "\n",
-    "# Boundary segments\n",
-    "for i in range(boundaries.shape[0]):\n",
-    "    if np.allclose(boundaries[i], 0):\n",
-    "        continue\n",
-    "    rx, ry, rz, length, _, dc, ds = boundaries[i]\n",
-    "    ax.plot(\n",
-    "        [rx - dc * length / 2, rx + dc * length / 2],\n",
-    "        [ry - ds * length / 2, ry + ds * length / 2],\n",
-    "        color=\"black\",\n",
-    "        linewidth=1,\n",
-    "        zorder=1,\n",
-    "    )\n",
-    "bnd_mask = np.any(boundaries != 0, axis=1)\n",
-    "if bnd_mask.any():\n",
-    "    ax.scatter(\n",
-    "        boundaries[bnd_mask, 0],\n",
-    "        boundaries[bnd_mask, 1],\n",
-    "        s=8,\n",
-    "        color=\"black\",\n",
-    "        alpha=0.6,\n",
-    "        label=f\"boundaries ({n_bounds})\",\n",
-    "        zorder=2,\n",
-    "    )\n",
-    "\n",
-    "# Partners\n",
-    "for i in range(partners.shape[0]):\n",
-    "    if np.allclose(partners[i], 0):\n",
-    "        continue\n",
-    "    rx, ry, rz, length, width, hc, hs, rel_vx, rel_vy = partners[i]\n",
-    "    heading = np.arctan2(hs, hc)\n",
-    "    rect = Rectangle(\n",
-    "        (-length / 2, -width / 2), length, width, facecolor=\"orange\", edgecolor=\"black\", alpha=0.6, zorder=9\n",
-    "    )\n",
-    "    rect.set_transform(plt.matplotlib.transforms.Affine2D().rotate(heading).translate(rx, ry) + ax.transData)\n",
-    "    ax.add_patch(rect)\n",
-    "    ax.annotate(f\"{rel_vx:.2f},{rel_vy:.2f}\", (rx, ry), fontsize=7, ha=\"center\", color=\"darkred\", zorder=12)\n",
-    "part_mask = np.any(partners != 0, axis=1)\n",
-    "if part_mask.any():\n",
-    "    ax.scatter(\n",
-    "        partners[part_mask, 0],\n",
-    "        partners[part_mask, 1],\n",
-    "        s=40,\n",
-    "        color=\"orange\",\n",
-    "        edgecolors=\"black\",\n",
-    "        label=f\"partners ({n_visible})\",\n",
-    "        zorder=8,\n",
-    "    )\n",
-    "\n",
-    "# Target waypoints\n",
-    "for wp in range(target.shape[0]):\n",
-    "    if np.allclose(target[wp], 0):\n",
-    "        continue\n",
-    "    marker = \"*\" if wp == 0 else \"o\"\n",
-    "    s = 200 if wp == 0 else 80\n",
-    "    color = \"red\" if wp == 0 else \"salmon\"\n",
-    "    ax.scatter(\n",
-    "        target[wp, 0],\n",
-    "        target[wp, 1],\n",
-    "        color=color,\n",
-    "        marker=marker,\n",
-    "        s=s,\n",
-    "        zorder=15,\n",
-    "        label=f\"target wp[{wp}]\" if wp < 3 else None,\n",
-    "    )\n",
-    "\n",
-    "# Traffic controls\n",
-    "for i in range(traffic_controls.shape[0]):\n",
-    "    if np.allclose(traffic_controls[i], 0):\n",
-    "        continue\n",
-    "    x1, y1, x2, y2, _, control_type, state = traffic_controls[i]\n",
-    "    if int(control_type) == binding.TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT:\n",
-    "        state_colors = {\n",
-    "            binding.TRAFFIC_CONTROL_STATE_UNKNOWN: \"gray\",\n",
-    "            binding.TRAFFIC_CONTROL_STATE_RED: \"red\",\n",
-    "            binding.TRAFFIC_CONTROL_STATE_YELLOW: \"yellow\",\n",
-    "            binding.TRAFFIC_CONTROL_STATE_GREEN: \"green\",\n",
-    "            binding.TRAFFIC_CONTROL_STATE_OFF: \"gray\",\n",
-    "        }\n",
-    "        ax.plot([x1, x2], [y1, y2], color=state_colors.get(int(state), \"gray\"), linewidth=3, zorder=15)\n",
-    "    else:\n",
-    "        accent = \"red\" if int(control_type) == binding.TRAFFIC_CONTROL_TYPE_STOP_SIGN else \"gold\"\n",
-    "        ax.plot([x1, x2], [y1, y2], color=\"black\", linewidth=4, zorder=14)\n",
-    "        ax.plot([x1, x2], [y1, y2], color=accent, linewidth=2.5, linestyle=\"--\", zorder=15)\n",
-    "\n",
-    "ax.set_xlim(-1, 1)\n",
-    "ax.set_ylim(-1, 1)\n",
-    "ax.set_aspect(\"equal\")\n",
-    "ax.set_xlabel(\"X (ego frame, normalized)\")\n",
-    "ax.set_ylabel(\"Y (ego frame, normalized)\")\n",
-    "ax.set_title(f\"All observed entities | agent={TRACKED_AGENT}, t={sample_t}\")\n",
-    "ax.legend(loc=\"upper right\", fontsize=8)\n",
-    "ax.grid(True, alpha=0.3)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "gsyrbg02vc",
-   "metadata": {},
-   "source": [
-    "### Ego + conditioning distributions across all agents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "wwisx2muaj",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Ego feature distributions across all agents, pooled over full rollout\n",
-    "ego_dim = binding.EGO_FEATURES\n",
-    "all_ego = buf_stoch[\"obs\"][:, :, :ego_dim].reshape(-1, ego_dim)  # (H*N, ego_dim)\n",
-    "\n",
-    "ego_labels = EGO_LABELS\n",
-    "\n",
-    "fig, axes = plt.subplots(2, len(ego_labels), figsize=(3.5 * len(ego_labels), 7))\n",
-    "\n",
-    "# Row 0: histograms\n",
-    "for i, label in enumerate(ego_labels):\n",
-    "    vals = all_ego[:, i]\n",
-    "    print(f\"{label}: mean={vals}\")\n",
-    "    axes[0, i].hist(vals, bins=60, edgecolor=\"black\", alpha=0.7, color=\"steelblue\")\n",
-    "    axes[0, i].set_title(label, fontsize=10)\n",
-    "    axes[0, i].set_xlabel(\"\")\n",
-    "    axes[0, i].tick_params(labelsize=7)\n",
-    "    axes[0, i].axvline(vals.mean(), color=\"red\", ls=\"--\", lw=1)\n",
-    "\n",
-    "# Row 1: boxplots per-agent (distribution across timesteps for each agent)\n",
-    "ego_per_agent = buf_stoch[\"obs\"][:, :, :ego_dim]  # (H, N, ego_dim)\n",
-    "for i, label in enumerate(ego_labels):\n",
-    "    data = [ego_per_agent[:, a, i] for a in range(N)]\n",
-    "    bp = axes[1, i].boxplot(\n",
-    "        data,\n",
-    "        showfliers=False,\n",
-    "        patch_artist=True,\n",
-    "        boxprops=dict(facecolor=\"steelblue\", alpha=0.5),\n",
-    "        medianprops=dict(color=\"red\"),\n",
-    "    )\n",
-    "    axes[1, i].set_xlabel(\"Agent\")\n",
-    "    axes[1, i].tick_params(labelsize=7)\n",
-    "    axes[1, i].set_title(f\"{label} per agent\", fontsize=9)\n",
-    "\n",
-    "fig.suptitle(\"Ego features: full rollout distributions\", fontsize=13)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Conditioning distributions across all agents (if enabled)\n",
-    "if rew_cond:\n",
-    "    cond_start = ego_dim\n",
-    "    cond_end = cond_start + binding.NUM_REWARD_COEFS\n",
-    "    all_cond = buf_stoch[\"obs\"][:, :, cond_start:cond_end].reshape(-1, binding.NUM_REWARD_COEFS)\n",
-    "\n",
-    "    cond_labels = COEF_NAMES\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(14, 5))\n",
-    "    parts = ax.violinplot(\n",
-    "        [all_cond[:, i] for i in range(binding.NUM_REWARD_COEFS)],\n",
-    "        positions=range(binding.NUM_REWARD_COEFS),\n",
-    "        showmeans=True,\n",
-    "        showmedians=True,\n",
-    "    )\n",
-    "    ax.set_xticks(range(binding.NUM_REWARD_COEFS))\n",
-    "    ax.set_xticklabels(cond_labels, rotation=45, ha=\"right\", fontsize=9)\n",
-    "    ax.set_ylabel(\"Normalized value\")\n",
-    "    ax.set_title(\"Conditioning coef distributions (all agents, full rollout)\")\n",
-    "    ax.grid(True, alpha=0.3, axis=\"y\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fu6dbejpxdu",
-   "metadata": {},
-   "source": [
-    "### Partner per-feature distributions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6g55putjnwo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Partner per-feature distributions (pooled over all agents + timesteps, visible only)\n",
-    "partner_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"length\", \"width\", \"heading_cos\", \"heading_sin\", \"rel_vx\", \"rel_vy\"]\n",
-    "obs_slots_partners_n = env.obs_slots_partners_n\n",
-    "pf = env.partner_features\n",
-    "\n",
-    "# Compute slices\n",
-    "_ego_d = binding.EGO_FEATURES\n",
-    "_cond_d = binding.NUM_REWARD_COEFS if rew_cond else 0\n",
-    "_tgt_f = binding.STATIC_TARGET_FEATURES if tgt_type == \"static\" else binding.DYNAMIC_TARGET_FEATURES\n",
-    "_tgt_d = n_tgt_wp * _tgt_f\n",
-    "_p_start = _ego_d + _cond_d + _tgt_d\n",
-    "_p_end = _p_start + obs_slots_partners_n * pf\n",
-    "\n",
-    "all_partners = buf_stoch[\"obs\"][:, :, _p_start:_p_end].reshape(\n",
-    "    -1, obs_slots_partners_n, pf\n",
-    ")  # (H*N, obs_slots_partners_n, 9)\n",
-    "# Mask: partner is visible if any feature != 0\n",
-    "visible_mask = np.any(all_partners != 0, axis=2)  # (H*N, 16)\n",
-    "visible_partners = all_partners[visible_mask]  # (K, 9) — all visible partner observations\n",
-    "\n",
-    "print(\n",
-    "    f\"Total partner obs: {all_partners.shape[0] * obs_slots_partners_n}, visible: {len(visible_partners)} \"\n",
-    "    f\"({100 * len(visible_partners) / (all_partners.shape[0] * obs_slots_partners_n):.1f}%)\"\n",
-    ")\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 5, figsize=(21, 8))\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "for i, label in enumerate(partner_labels):\n",
-    "    vals = visible_partners[:, i]\n",
-    "    axes[i].hist(vals, bins=80, edgecolor=\"black\", alpha=0.7, color=\"darkorange\")\n",
-    "    axes[i].set_title(f\"{label} (n={len(vals)})\", fontsize=10)\n",
-    "    axes[i].axvline(vals.mean(), color=\"red\", ls=\"--\", lw=1, label=f\"mean={vals.mean():.3f}\")\n",
-    "    axes[i].legend(fontsize=7)\n",
-    "    axes[i].tick_params(labelsize=7)\n",
-    "\n",
-    "# rel_x vs rel_y scatter in last panel\n",
-    "pos_ax = axes[len(partner_labels)]\n",
-    "pos_ax.scatter(visible_partners[:, 0], visible_partners[:, 1], s=1, alpha=0.15, color=\"darkorange\")\n",
-    "pos_ax.set_xlabel(\"rel_x\")\n",
-    "pos_ax.set_ylabel(\"rel_y\")\n",
-    "pos_ax.set_title(\"Partner positions (ego frame)\")\n",
-    "pos_ax.set_aspect(\"equal\")\n",
-    "pos_ax.grid(True, alpha=0.3)\n",
-    "\n",
-    "fig.suptitle(\"Partner features: all visible, full rollout\", fontsize=13)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Partner count distribution across (timestep, agent)\n",
-    "partner_counts = visible_mask.sum(axis=1)  # (H*N,)\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
-    "axes[0].hist(partner_counts, bins=range(obs_slots_partners_n + 2), edgecolor=\"black\", alpha=0.7, color=\"darkorange\")\n",
-    "axes[0].set_xlabel(\"Visible partners\")\n",
-    "axes[0].set_ylabel(\"Count\")\n",
-    "axes[0].set_title(\"Partner count distribution (per agent per step)\")\n",
-    "\n",
-    "# Partner distance distribution\n",
-    "dists = np.sqrt(visible_partners[:, 0] ** 2 + visible_partners[:, 1] ** 2)\n",
-    "axes[1].hist(dists, bins=80, edgecolor=\"black\", alpha=0.7, color=\"coral\")\n",
-    "axes[1].set_xlabel(\"Distance (normalized)\")\n",
-    "axes[1].set_ylabel(\"Count\")\n",
-    "axes[1].set_title(f\"Partner distance distribution (mean={dists.mean():.3f})\")\n",
-    "axes[1].axvline(dists.mean(), color=\"red\", ls=\"--\", lw=1)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5n8a8dwmna3",
-   "metadata": {},
-   "source": [
-    "### Road (lanes + boundaries) and target distributions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4xgikpryeg",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Road per-feature distributions (lanes + boundaries)\n",
-    "road_labels = [\"rel_x\", \"rel_y\", \"rel_z\", \"seg_length\", \"seg_width\", \"dir_cos\", \"dir_sin\"]\n",
-    "rf = env.road_features\n",
-    "max_lanes = env.obs_slots_lane_kept\n",
-    "max_bounds = env.obs_slots_boundary_kept\n",
-    "\n",
-    "_l_start = _p_end\n",
-    "_l_end = _l_start + max_lanes * rf\n",
-    "_b_start = _l_end\n",
-    "_b_end = _b_start + max_bounds * rf\n",
-    "\n",
-    "all_lanes = buf_stoch[\"obs\"][:, :, _l_start:_l_end].reshape(-1, max_lanes, rf)\n",
-    "all_bounds = buf_stoch[\"obs\"][:, :, _b_start:_b_end].reshape(-1, max_bounds, rf)\n",
-    "\n",
-    "vis_lanes = all_lanes[np.any(all_lanes != 0, axis=2)]\n",
-    "vis_bounds = all_bounds[np.any(all_bounds != 0, axis=2)]\n",
-    "\n",
-    "print(\n",
-    "    f\"Lanes: {len(vis_lanes)} visible / {all_lanes.shape[0] * max_lanes} total \"\n",
-    "    f\"({100 * len(vis_lanes) / (all_lanes.shape[0] * max_lanes):.1f}%)\"\n",
-    ")\n",
-    "print(\n",
-    "    f\"Boundaries: {len(vis_bounds)} visible / {all_bounds.shape[0] * max_bounds} total \"\n",
-    "    f\"({100 * len(vis_bounds) / (all_bounds.shape[0] * max_bounds):.1f}%)\"\n",
-    ")\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 7, figsize=(28, 8))\n",
-    "for i, label in enumerate(road_labels):\n",
-    "    # Lanes\n",
-    "    axes[0, i].hist(vis_lanes[:, i], bins=80, edgecolor=\"black\", alpha=0.7, color=\"silver\")\n",
-    "    axes[0, i].set_title(f\"lane {label}\", fontsize=9)\n",
-    "    axes[0, i].axvline(vis_lanes[:, i].mean(), color=\"red\", ls=\"--\", lw=1)\n",
-    "    axes[0, i].tick_params(labelsize=7)\n",
-    "    # Boundaries\n",
-    "    axes[1, i].hist(vis_bounds[:, i], bins=80, edgecolor=\"black\", alpha=0.7, color=\"dimgray\")\n",
-    "    axes[1, i].set_title(f\"boundary {label}\", fontsize=9)\n",
-    "    axes[1, i].axvline(vis_bounds[:, i].mean(), color=\"red\", ls=\"--\", lw=1)\n",
-    "    axes[1, i].tick_params(labelsize=7)\n",
-    "\n",
-    "fig.suptitle(\"Road features: all visible, full rollout (top=lanes, bottom=boundaries)\", fontsize=13)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Spatial scatter: lane vs boundary positions (pooled)\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
-    "axes[0].scatter(vis_lanes[:, 0], vis_lanes[:, 1], s=0.5, alpha=0.05, color=\"gray\")\n",
-    "axes[0].set_xlabel(\"rel_x\")\n",
-    "axes[0].set_ylabel(\"rel_y\")\n",
-    "axes[0].set_title(f\"Lane segment positions (n={len(vis_lanes)})\")\n",
-    "axes[0].set_aspect(\"equal\")\n",
-    "axes[0].grid(True, alpha=0.3)\n",
-    "\n",
-    "axes[1].scatter(vis_bounds[:, 0], vis_bounds[:, 1], s=0.5, alpha=0.05, color=\"black\")\n",
-    "axes[1].set_xlabel(\"rel_x\")\n",
-    "axes[1].set_ylabel(\"rel_y\")\n",
-    "axes[1].set_title(f\"Boundary segment positions (n={len(vis_bounds)})\")\n",
-    "axes[1].set_aspect(\"equal\")\n",
-    "axes[1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Lane + boundary segment length comparison\n",
-    "axes[2].hist(vis_lanes[:, 2], bins=80, alpha=0.6, color=\"silver\", edgecolor=\"black\", label=\"lanes\")\n",
-    "axes[2].hist(vis_bounds[:, 2], bins=80, alpha=0.6, color=\"dimgray\", edgecolor=\"black\", label=\"boundaries\")\n",
-    "axes[2].set_xlabel(\"Segment length (normalized)\")\n",
-    "axes[2].set_ylabel(\"Count\")\n",
-    "axes[2].set_title(\"Segment length distribution\")\n",
-    "axes[2].legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Target distributions across all agents, full rollout\n",
-    "_tgt_start = _ego_d + _cond_d\n",
-    "_tgt_end = _tgt_start + _tgt_d\n",
-    "all_target = buf_stoch[\"obs\"][:, :, _tgt_start:_tgt_end].reshape(-1, n_tgt_wp, _tgt_f)\n",
-    "\n",
-    "if tgt_type == \"static\":\n",
-    "    tgt_flabels = [\"rel_x\", \"rel_y\", \"rel_z\"]\n",
-    "else:\n",
-    "    tgt_flabels = [\"rel_x\", \"rel_y\", \"rel_z\", \"heading_cos\", \"heading_sin\"]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, n_tgt_wp + 1, figsize=(5 * (n_tgt_wp + 1), 4))\n",
-    "\n",
-    "for wp in range(n_tgt_wp):\n",
-    "    wp_data = all_target[:, wp, :]\n",
-    "    active = np.any(wp_data != 0, axis=1)\n",
-    "    wp_active = wp_data[active]\n",
-    "    dist = np.sqrt(wp_active[:, 0] ** 2 + wp_active[:, 1] ** 2) if len(wp_active) > 0 else np.array([])\n",
-    "    axes[wp].hist(dist, bins=60, edgecolor=\"black\", alpha=0.7, color=[\"red\", \"salmon\", \"lightsalmon\"][wp % 3])\n",
-    "    axes[wp].set_title(f\"wp[{wp}] distance (n={len(wp_active)}/{len(wp_data)})\", fontsize=10)\n",
-    "    axes[wp].set_xlabel(\"Distance (normalized)\")\n",
-    "\n",
-    "# All waypoints x-y scatter\n",
-    "for wp in range(n_tgt_wp):\n",
-    "    wp_data = all_target[:, wp, :]\n",
-    "    active = np.any(wp_data != 0, axis=1)\n",
-    "    wp_active = wp_data[active]\n",
-    "    if len(wp_active) > 0:\n",
-    "        axes[n_tgt_wp].scatter(wp_active[:, 0], wp_active[:, 1], s=1, alpha=0.1, label=f\"wp[{wp}]\")\n",
-    "axes[n_tgt_wp].set_xlabel(\"rel_x\")\n",
-    "axes[n_tgt_wp].set_ylabel(\"rel_y\")\n",
-    "axes[n_tgt_wp].set_title(\"Target positions (ego frame)\")\n",
-    "axes[n_tgt_wp].set_aspect(\"equal\")\n",
-    "axes[n_tgt_wp].legend(fontsize=8)\n",
-    "axes[n_tgt_wp].grid(True, alpha=0.3)\n",
-    "\n",
-    "fig.suptitle(\"Target waypoint distributions (all agents, full rollout)\", fontsize=13)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "csj7ji1j6ed",
-   "metadata": {},
-   "source": [
-    "### Observation sparsity and layer occupancy heatmaps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3xbi2pbsfyv",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sparsity heatmap: fraction of nonzero per layer, per agent, over time\n",
-    "layer_names = [\"partners\", \"lanes\", \"boundaries\"]\n",
-    "layer_slices = [\n",
-    "    (_p_start, _p_end, env.obs_slots_partners_n, env.partner_features),\n",
-    "    (_l_start, _l_end, env.obs_slots_lane_kept, env.road_features),\n",
-    "    (_b_start, _b_end, env.obs_slots_boundary_kept, env.road_features),\n",
-    "]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
-    "for ax, name, (s, e, n_obj, n_feat) in zip(axes, layer_names, layer_slices):\n",
-    "    # (H, N) -> fraction of visible objects per (timestep, agent)\n",
-    "    raw = buf_stoch[\"obs\"][:, :, s:e].reshape(HORIZON, env.num_agents, n_obj, n_feat)\n",
-    "    occupancy = np.any(raw != 0, axis=3).sum(axis=2) / n_obj  # (H, N)\n",
-    "    im = ax.imshow(occupancy.T, aspect=\"auto\", cmap=\"YlOrRd\", interpolation=\"nearest\", vmin=0, vmax=1)\n",
-    "    ax.set_xlabel(\"Step\")\n",
-    "    ax.set_ylabel(\"Agent\")\n",
-    "    ax.set_title(f\"{name} occupancy (frac visible)\")\n",
-    "    plt.colorbar(im, ax=ax)\n",
-    "\n",
-    "plt.suptitle(\"Per-layer occupancy heatmaps (fraction of max slots filled)\", fontsize=13)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Per-layer mean occupancy over time\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 4))\n",
-    "\n",
-    "# Mean across agents\n",
-    "for name, (s, e, n_obj, n_feat) in zip(layer_names, layer_slices):\n",
-    "    raw = buf_stoch[\"obs\"][:, :, s:e].reshape(HORIZON, env.num_agents, n_obj, n_feat)\n",
-    "    occ_mean = np.any(raw != 0, axis=3).sum(axis=2).mean(axis=1)  # (H,)\n",
-    "    axes[0].plot(occ_mean, label=name, alpha=0.8)\n",
-    "axes[0].set_xlabel(\"Step\")\n",
-    "axes[0].set_ylabel(\"Mean visible count\")\n",
-    "axes[0].set_title(\"Mean occupancy over time (across agents)\")\n",
-    "axes[0].legend()\n",
-    "axes[0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Mean across timesteps (per agent)\n",
-    "for name, (s, e, n_obj, n_feat) in zip(layer_names, layer_slices):\n",
-    "    raw = buf_stoch[\"obs\"][:, :, s:e].reshape(HORIZON, env.num_agents, n_obj, n_feat)\n",
-    "    occ_per_agent = np.any(raw != 0, axis=3).sum(axis=2).mean(axis=0)  # (N,)\n",
-    "    axes[1].bar(range(N), occ_per_agent, alpha=0.5, label=name)\n",
-    "axes[1].set_xlabel(\"Agent\")\n",
-    "axes[1].set_ylabel(\"Mean visible count\")\n",
-    "axes[1].set_title(\"Mean occupancy per agent (across timesteps)\")\n",
-    "axes[1].legend()\n",
-    "axes[1].grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# Full obs sparsity: fraction of zero features per obs dimension, pooled\n",
-    "all_flat = buf_stoch[\"obs\"].reshape(-1, obs_dim)  # (H*N, obs_dim)\n",
-    "zero_frac = (all_flat == 0).mean(axis=0)  # (obs_dim,)\n",
-    "fig, ax = plt.subplots(figsize=(18, 3))\n",
-    "ax.bar(range(obs_dim), zero_frac, width=1.0, color=\"steelblue\", alpha=0.7)\n",
-    "# Annotate layer boundaries\n",
-    "prev_e = 0\n",
-    "for name, (s, e) in slices.items():\n",
-    "    ax.axvline(s, color=\"red\", ls=\"--\", lw=0.5, alpha=0.7)\n",
-    "    mid = (s + e) / 2\n",
-    "    ax.text(mid, 1.02, name, ha=\"center\", va=\"bottom\", fontsize=7, rotation=0, color=\"red\")\n",
-    "    prev_e = e\n",
-    "ax.set_xlim(0, obs_dim)\n",
-    "ax.set_ylim(0, 1.1)\n",
-    "ax.set_xlabel(\"Obs dimension index\")\n",
-    "ax.set_ylabel(\"Fraction zero\")\n",
-    "ax.set_title(\"Per-dimension sparsity (fraction zero across full rollout)\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-8",
-   "metadata": {},
-   "source": [
-    "## Policy outputs over time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compute action probs over time for tracked agent (stochastic rollout)\n",
-    "n_actions = env.single_action_space.nvec[0] if not is_continuous else 1\n",
-    "action_probs_time = np.zeros((HORIZON, n_actions))\n",
-    "for t in range(HORIZON):\n",
-    "    obs_t = torch.FloatTensor(buf_stoch[\"obs\"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]).to(device)\n",
-    "    with torch.no_grad():\n",
-    "        logits_list, _ = policy(obs_t)\n",
-    "    logits = logits_list[0] if isinstance(logits_list, (list, tuple)) else logits_list\n",
-    "    action_probs_time[t] = F.softmax(logits, dim=-1).cpu().numpy().flatten()\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n",
-    "\n",
-    "# Action distribution heatmap\n",
-    "im = axes[0, 0].imshow(action_probs_time.T, aspect=\"auto\", cmap=\"viridis\", interpolation=\"nearest\")\n",
-    "axes[0, 0].set_xlabel(\"Step\")\n",
-    "axes[0, 0].set_ylabel(\"Action ID\")\n",
-    "axes[0, 0].set_title(f\"Action prob heatmap | agent={TRACKED_AGENT}\")\n",
-    "plt.colorbar(im, ax=axes[0, 0])\n",
-    "\n",
-    "# Entropy over time\n",
-    "axes[0, 1].plot(buf_stoch[\"entropy\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.8)\n",
-    "axes[0, 1].set_xlabel(\"Step\")\n",
-    "axes[0, 1].set_ylabel(\"Entropy\")\n",
-    "axes[0, 1].set_title(\"Entropy over time\")\n",
-    "axes[0, 1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Value over time\n",
-    "axes[1, 0].plot(buf_stoch[\"values\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.8)\n",
-    "axes[1, 0].plot(buf_det[\"values\"][:, TRACKED_AGENT], label=\"deterministic\", alpha=0.8)\n",
-    "axes[1, 0].set_xlabel(\"Step\")\n",
-    "axes[1, 0].set_ylabel(\"Value\")\n",
-    "axes[1, 0].set_title(\"Value predictions over time\")\n",
-    "axes[1, 0].legend()\n",
-    "axes[1, 0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Actions over time: deterministic vs stochastic\n",
-    "axes[1, 1].step(range(HORIZON), buf_stoch[\"actions\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.7)\n",
-    "axes[1, 1].step(range(HORIZON), buf_det[\"actions\"][:, TRACKED_AGENT], label=\"deterministic\", alpha=0.7)\n",
-    "axes[1, 1].set_xlabel(\"Step\")\n",
-    "axes[1, 1].set_ylabel(\"Action\")\n",
-    "axes[1, 1].set_title(\"Selected action over time\")\n",
-    "axes[1, 1].legend()\n",
-    "axes[1, 1].grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-10",
-   "metadata": {},
-   "source": [
-    "## Rewards and returns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-11",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))\n",
-    "\n",
-    "# Per-step mean reward\n",
-    "axes[0, 0].plot(buf_stoch[\"rewards\"].mean(axis=1), label=\"stochastic\", alpha=0.8)\n",
-    "axes[0, 0].plot(buf_det[\"rewards\"].mean(axis=1), label=\"deterministic\", alpha=0.8)\n",
-    "axes[0, 0].set_xlabel(\"Step\")\n",
-    "axes[0, 0].set_ylabel(\"Mean reward\")\n",
-    "axes[0, 0].set_title(\"Mean reward per step\")\n",
-    "axes[0, 0].legend()\n",
-    "axes[0, 0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Reward heatmap (stochastic)\n",
-    "im = axes[0, 1].imshow(buf_stoch[\"rewards\"].T, aspect=\"auto\", cmap=\"RdYlGn\", interpolation=\"nearest\")\n",
-    "axes[0, 1].set_xlabel(\"Step\")\n",
-    "axes[0, 1].set_ylabel(\"Agent\")\n",
-    "axes[0, 1].set_title(\"Reward heatmap (stochastic)\")\n",
-    "plt.colorbar(im, ax=axes[0, 1])\n",
-    "\n",
-    "# Cumulative return per agent\n",
-    "cum_ret_stoch = buf_stoch[\"rewards\"].sum(axis=0)\n",
-    "cum_ret_det = buf_det[\"rewards\"].sum(axis=0)\n",
-    "axes[0, 2].hist(cum_ret_stoch, bins=30, alpha=0.6, label=\"stochastic\", edgecolor=\"black\")\n",
-    "axes[0, 2].hist(cum_ret_det, bins=30, alpha=0.6, label=\"deterministic\", edgecolor=\"black\")\n",
-    "axes[0, 2].set_xlabel(\"Cumulative return\")\n",
-    "axes[0, 2].set_ylabel(\"Count\")\n",
-    "axes[0, 2].set_title(\"Return distribution across agents\")\n",
-    "axes[0, 2].legend()\n",
-    "\n",
-    "# Reward distribution histogram\n",
-    "axes[1, 0].hist(buf_stoch[\"rewards\"].flatten(), bins=50, alpha=0.7, edgecolor=\"black\")\n",
-    "axes[1, 0].set_xlabel(\"Reward\")\n",
-    "axes[1, 0].set_ylabel(\"Count\")\n",
-    "axes[1, 0].set_title(\"Per-step reward distribution (stochastic)\")\n",
-    "axes[1, 0].set_yscale(\"log\")\n",
-    "\n",
-    "# Terminal/truncation timeline\n",
-    "axes[1, 1].plot(buf_stoch[\"terminals\"].sum(axis=1), label=\"terminals\", alpha=0.8)\n",
-    "axes[1, 1].plot(buf_stoch[\"truncations\"].sum(axis=1), label=\"truncations\", alpha=0.8)\n",
-    "axes[1, 1].set_xlabel(\"Step\")\n",
-    "axes[1, 1].set_ylabel(\"Count\")\n",
-    "axes[1, 1].set_title(\"Terminals/Truncations per step (stochastic)\")\n",
-    "axes[1, 1].legend()\n",
-    "axes[1, 1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Tracked agent reward\n",
-    "axes[1, 2].plot(buf_stoch[\"rewards\"][:, TRACKED_AGENT], label=\"stochastic\", alpha=0.8)\n",
-    "axes[1, 2].plot(buf_det[\"rewards\"][:, TRACKED_AGENT], label=\"deterministic\", alpha=0.8)\n",
-    "axes[1, 2].set_xlabel(\"Step\")\n",
-    "axes[1, 2].set_ylabel(\"Reward\")\n",
-    "axes[1, 2].set_title(f\"Reward over time | agent={TRACKED_AGENT}\")\n",
-    "axes[1, 2].legend()\n",
-    "axes[1, 2].grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-12",
-   "metadata": {},
-   "source": [
-    "## Episode metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-13",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Collect episode-level metrics from the C logging\n",
-    "log_stoch = binding.vec_log(env.c_envs, N)\n",
-    "\n",
-    "# eval_mode=1 returns a list of per-env dicts; aggregate by averaging\n",
-    "if isinstance(log_stoch, list) and log_stoch:\n",
-    "    all_keys = set(k for d in log_stoch for k in d if isinstance(d[k], (int, float)))\n",
-    "    log_stoch = {k: np.mean([d[k] for d in log_stoch if k in d]) for k in all_keys}\n",
-    "\n",
-    "if log_stoch:\n",
-    "    print(\"Episode metrics (after stochastic rollout):\")\n",
-    "    for k, v in sorted(log_stoch.items()):\n",
-    "        if isinstance(v, (int, float)):\n",
-    "            print(f\"  {k}: {v:.4f}\")\n",
-    "\n",
-    "    # Bar chart of key metrics\n",
-    "    keys = [\"score\", \"collision_rate\", \"offroad_rate\", \"completion_rate\", \"dnf_rate\"]\n",
-    "    vals = [log_stoch.get(k, 0) for k in keys]\n",
-    "    fig, ax = plt.subplots(figsize=(10, 4))\n",
-    "    bars = ax.bar(keys, vals, edgecolor=\"black\", alpha=0.7, color=[\"green\", \"red\", \"orange\", \"blue\", \"gray\"])\n",
-    "    ax.set_ylabel(\"Rate\")\n",
-    "    ax.set_title(\"Episode Metrics\")\n",
-    "    for bar, v in zip(bars, vals):\n",
-    "        ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.01, f\"{v:.3f}\", ha=\"center\", fontsize=10)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "else:\n",
-    "    print(\"No episode metrics available yet (not enough episodes completed)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-14",
-   "metadata": {},
-   "source": [
-    "## Value predictions vs actual returns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gamma = config[\"train\"].get(\"gamma\", 0.98)\n",
-    "lam = config[\"train\"].get(\"gae_lambda\", 0.95)\n",
-    "\n",
-    "\n",
-    "def compute_gae(rewards, values, terminals, truncations, gamma, lam):\n",
-    "    H, N = rewards.shape\n",
-    "    advantages = np.zeros_like(rewards)\n",
-    "    last_gae = np.zeros(N)\n",
-    "    for t in reversed(range(H - 1)):\n",
-    "        done = np.maximum(terminals[t + 1], truncations[t + 1])\n",
-    "        next_non_terminal = 1.0 - done\n",
-    "        delta = rewards[t + 1] + gamma * values[t + 1] * next_non_terminal - values[t]\n",
-    "        last_gae = delta + gamma * lam * last_gae * next_non_terminal\n",
-    "        advantages[t] = last_gae\n",
-    "    return advantages\n",
-    "\n",
-    "\n",
-    "adv_stoch = compute_gae(\n",
-    "    buf_stoch[\"rewards\"], buf_stoch[\"values\"], buf_stoch[\"terminals\"], buf_stoch[\"truncations\"], gamma, lam\n",
-    ")\n",
-    "returns_stoch = adv_stoch + buf_stoch[\"values\"]\n",
-    "\n",
-    "pred_v = buf_stoch[\"values\"].flatten()\n",
-    "actual_r = returns_stoch.flatten()\n",
-    "\n",
-    "var_actual = np.var(actual_r)\n",
-    "explained_var = 1 - np.var(actual_r - pred_v) / (var_actual + 1e-8) if var_actual > 1e-8 else 0.0\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
-    "\n",
-    "# Scatter: predicted vs actual\n",
-    "axes[0].scatter(actual_r, pred_v, alpha=0.2, s=5)\n",
-    "lims = [min(actual_r.min(), pred_v.min()), max(actual_r.max(), pred_v.max())]\n",
-    "axes[0].plot(lims, lims, \"r--\", label=\"perfect\")\n",
-    "axes[0].set_xlabel(\"Actual return\")\n",
-    "axes[0].set_ylabel(\"Predicted value\")\n",
-    "axes[0].set_title(f\"Value accuracy (EV: {explained_var:.4f})\")\n",
-    "axes[0].legend()\n",
-    "axes[0].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Value error over time\n",
-    "value_error = np.abs(returns_stoch - buf_stoch[\"values\"]).mean(axis=1)\n",
-    "axes[1].plot(value_error)\n",
-    "axes[1].set_xlabel(\"Step\")\n",
-    "axes[1].set_ylabel(\"Mean |error|\")\n",
-    "axes[1].set_title(\"Value prediction error over time\")\n",
-    "axes[1].grid(True, alpha=0.3)\n",
-    "\n",
-    "# Advantage distribution\n",
-    "axes[2].hist(adv_stoch.flatten(), bins=50, edgecolor=\"black\", alpha=0.7)\n",
-    "axes[2].set_xlabel(\"Advantage\")\n",
-    "axes[2].set_ylabel(\"Count\")\n",
-    "axes[2].set_title(f\"Advantage distribution (std={adv_stoch.std():.4f})\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(f\"Explained variance: {explained_var:.4f}\")\n",
-    "print(f\"Value MSE: {np.mean((actual_r - pred_v) ** 2):.6f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-16",
-   "metadata": {},
-   "source": [
-    "## Agent trajectories"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-17",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N_TRAJ = min(16, N)  # number of agents to plot\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 7))\n",
-    "\n",
-    "for i in range(N_TRAJ):\n",
-    "    color = plt.cm.tab20(i % 20)\n",
-    "    # Stochastic\n",
-    "    axes[0].plot(buf_stoch[\"positions_x\"][:, i], buf_stoch[\"positions_y\"][:, i], alpha=0.6, color=color, linewidth=1)\n",
-    "    axes[0].scatter(\n",
-    "        buf_stoch[\"positions_x\"][0, i], buf_stoch[\"positions_y\"][0, i], color=color, s=30, marker=\"o\", zorder=5\n",
-    "    )  # start\n",
-    "    # Deterministic\n",
-    "    axes[1].plot(buf_det[\"positions_x\"][:, i], buf_det[\"positions_y\"][:, i], alpha=0.6, color=color, linewidth=1)\n",
-    "    axes[1].scatter(buf_det[\"positions_x\"][0, i], buf_det[\"positions_y\"][0, i], color=color, s=30, marker=\"o\", zorder=5)\n",
-    "\n",
-    "axes[0].set_title(f\"Stochastic trajectories (N={N_TRAJ})\")\n",
-    "axes[1].set_title(f\"Deterministic trajectories (N={N_TRAJ})\")\n",
-    "for ax in axes:\n",
-    "    ax.set_xlabel(\"X\")\n",
-    "    ax.set_ylabel(\"Y\")\n",
-    "    ax.set_aspect(\"equal\")\n",
-    "    ax.grid(True, alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# ADE vs ground truth if scenario_length is set\n",
-    "if config[\"env\"].get(\"scenario_length\"):\n",
-    "    try:\n",
-    "        gt = env.get_ground_truth_trajectories()\n",
-    "        # gt['x'] shape: (N, 1, T), positions shape: (T, N)\n",
-    "        gt_x = gt[\"x\"][:, 0, :].T  # (T, N)\n",
-    "        gt_y = gt[\"y\"][:, 0, :].T\n",
-    "        gt_valid = gt[\"valid\"][:, 0, :].T\n",
-    "        T_gt = gt_x.shape[0]\n",
-    "        T_use = min(T_gt, HORIZON)\n",
-    "\n",
-    "        disp = np.sqrt(\n",
-    "            (buf_stoch[\"positions_x\"][:T_use] - gt_x[:T_use]) ** 2\n",
-    "            + (buf_stoch[\"positions_y\"][:T_use] - gt_y[:T_use]) ** 2\n",
-    "        )\n",
-    "        valid_mask = gt_valid[:T_use] > 0\n",
-    "        if valid_mask.sum() > 0:\n",
-    "            ade = disp[valid_mask].mean()\n",
-    "            print(f\"ADE (stochastic vs ground truth): {ade:.3f}m\")\n",
-    "            ade_per_agent = np.array(\n",
-    "                [disp[:, i][valid_mask[:, i]].mean() for i in range(N) if valid_mask[:, i].sum() > 0]\n",
-    "            )\n",
-    "            plt.figure(figsize=(8, 3))\n",
-    "            plt.hist(ade_per_agent, bins=30, edgecolor=\"black\", alpha=0.7)\n",
-    "            plt.xlabel(\"ADE (m)\")\n",
-    "            plt.ylabel(\"Count\")\n",
-    "            plt.title(f\"Per-agent ADE distribution (mean={ade:.3f}m)\")\n",
-    "            plt.tight_layout()\n",
-    "            plt.show()\n",
-    "        else:\n",
-    "            print(\"No valid ground truth timesteps to compute ADE\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"Could not compute ADE: {e}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea90af09",
-   "metadata": {},
-   "source": [
-    "## Encoder analysis — what the policy encodes\n",
-    "\n",
-    "Each obs layer has its own encoder projecting raw features → embedding width:\n",
-    "- **ego** and **conditioning** (reward coefs + target): single vector, no pooling.\n",
-    "- **partners / lanes / boundaries / traffic**: per-slot encoder, padded slots masked to `-inf`, then **max-pooled** across slots → one embedding. Fully-padded layers are zeroed.\n",
-    "\n",
-    "The max-pool means each embedding dim is \"won\" by exactly one slot (object). Below we inspect:\n",
-    "1. Encoder inventory (in/out dims, params).\n",
-    "2. **What survives the max-pool**: which slot wins per dim, per-dim winner entropy (slot-specialized vs. spread), and where the dominant objects sit in ego frame.\n",
-    "3. **Embedding space**: per-encoder contribution (L2 norm), active/dead dims, silence rate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7eb06a8d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ── Setup: capture per-encoder embeddings + reconstruct the max-pool ──\n",
-    "bb = policy.actor_backbone\n",
-    "ego_dim = policy.ego_dim\n",
-    "PAD = -1.0  # PADDED_OBSERVATION_VALUE\n",
-    "\n",
-    "# Flat batch of observations from the stochastic rollout\n",
-    "obs_flat = buf_stoch[\"obs\"].reshape(-1, obs_dim)\n",
-    "rng = np.random.default_rng(0)\n",
-    "sel = rng.choice(obs_flat.shape[0], size=min(4096, obs_flat.shape[0]), replace=False)\n",
-    "obs_batch = torch.FloatTensor(obs_flat[sel]).to(device)\n",
-    "B = obs_batch.shape[0]\n",
-    "\n",
-    "# Encoder inventory: (name, module, raw_in_features, n_slots, is_set)\n",
-    "enc_inventory = [(\"ego\", bb.ego_encoder, ego_dim, 1, False)]\n",
-    "if bb.obs_slots_lane_kept > 0:\n",
-    "    enc_inventory.append((\"lane\", bb.lane_encoder, bb.road_features_count, bb.obs_slots_lane_kept, True))\n",
-    "if bb.obs_slots_boundary_kept > 0:\n",
-    "    enc_inventory.append((\"boundary\", bb.boundary_encoder, bb.road_features_count, bb.obs_slots_boundary_kept, True))\n",
-    "if bb.obs_slots_partners_n > 0:\n",
-    "    enc_inventory.append((\"partner\", bb.partner_encoder, bb.partner_features_count, bb.obs_slots_partners_n, True))\n",
-    "if bb.obs_slots_traffic_controls_n > 0:\n",
-    "    enc_inventory.append(\n",
-    "        (\n",
-    "            \"traffic\",\n",
-    "            bb.traffic_control_encoder,\n",
-    "            bb.traffic_control_features_after_onehot,\n",
-    "            bb.obs_slots_traffic_controls_n,\n",
-    "            True,\n",
-    "        )\n",
-    "    )\n",
-    "if bb.target_dim > 0:\n",
-    "    enc_inventory.append((\"conditioning\", bb.target_encoder, bb.target_dim, 1, False))\n",
-    "\n",
-    "enc_names = [n for n, *_ in enc_inventory]\n",
-    "set_encs = [n for n, _, _, _, is_set in enc_inventory if is_set]\n",
-    "\n",
-    "print(f\"{'encoder':>13s} | {'raw_in':>6s} | {'emb_out':>7s} | {'slots':>5s} | {'pooled':>6s} | {'params':>9s}\")\n",
-    "print(\"-\" * 66)\n",
-    "for name, mod, rin, nslots, is_set in enc_inventory:\n",
-    "    nparam = sum(p.numel() for p in mod.parameters())\n",
-    "    print(\n",
-    "        f\"{name:>13s} | {rin:>6d} | {mod[-1].out_features:>7d} | {nslots:>5d} | {('max' if is_set else '-'):>6s} | {nparam:>9,d}\"\n",
-    "    )\n",
-    "print(\n",
-    "    f\"\\nBackbone input = {sum(mod[-1].out_features for _, mod, _, _, _ in enc_inventory)} -> backbone -> {bb.out_dim}\"\n",
-    ")\n",
-    "\n",
-    "# Capture pre-pool encoder outputs via forward hooks\n",
-    "captured = {}\n",
-    "\n",
-    "\n",
-    "def _hook(name):\n",
-    "    def fn(m, i, o):\n",
-    "        captured[name] = o.detach()\n",
-    "\n",
-    "    return fn\n",
-    "\n",
-    "\n",
-    "handles = [mod.register_forward_hook(_hook(name)) for name, mod, *_ in enc_inventory]\n",
-    "policy.eval()\n",
-    "with torch.no_grad():\n",
-    "    policy(obs_batch)\n",
-    "for h in handles:\n",
-    "    h.remove()\n",
-    "\n",
-    "# Reconstruct slot slices (same order as DriveBackbone.forward) + pad masks\n",
-    "partner_dim = bb.obs_slots_partners_n * bb.partner_features_count\n",
-    "lane_dim = bb.obs_slots_lane_kept * bb.road_features_count\n",
-    "boundary_dim = bb.obs_slots_boundary_kept * bb.road_features_count\n",
-    "traffic_dim = bb.obs_slots_traffic_controls_n * bb.traffic_control_features_count\n",
-    "_s = ego_dim + bb.target_dim\n",
-    "sl = {}\n",
-    "sl[\"partner\"] = (_s, _s + partner_dim, bb.obs_slots_partners_n, bb.partner_features_count)\n",
-    "_s += partner_dim\n",
-    "sl[\"lane\"] = (_s, _s + lane_dim, bb.obs_slots_lane_kept, bb.road_features_count)\n",
-    "_s += lane_dim\n",
-    "sl[\"boundary\"] = (_s, _s + boundary_dim, bb.obs_slots_boundary_kept, bb.road_features_count)\n",
-    "_s += boundary_dim\n",
-    "sl[\"traffic\"] = (_s, _s + traffic_dim, bb.obs_slots_traffic_controls_n, bb.traffic_control_features_count)\n",
-    "_s += traffic_dim\n",
-    "\n",
-    "raw, pad, pooled, winners, valid_sample = {}, {}, {}, {}, {}\n",
-    "for name in set_encs:\n",
-    "    s, e, ns, nf = sl[name]\n",
-    "    obj = obs_batch[:, s:e].view(B, ns, nf)\n",
-    "    raw[name] = obj\n",
-    "    if name == \"traffic\":\n",
-    "        cont = obj[:, :, : bb.traffic_control_continuous_features]\n",
-    "        typ = obj[:, :, bb.traffic_control_continuous_features]\n",
-    "        st = obj[:, :, bb.traffic_control_continuous_features + 1]\n",
-    "        pad[name] = (\n",
-    "            (cont == PAD).all(dim=2)\n",
-    "            & (typ == binding.TRAFFIC_CONTROL_TYPE_NONE)\n",
-    "            & (st == binding.TRAFFIC_CONTROL_STATE_UNKNOWN)\n",
-    "        )\n",
-    "    else:\n",
-    "        pad[name] = (obj == PAD).all(dim=2)\n",
-    "    masked = captured[name].masked_fill(pad[name].unsqueeze(2), -torch.inf)\n",
-    "    vm = (~pad[name]).any(dim=1)\n",
-    "    valid_sample[name] = vm\n",
-    "    winners[name] = masked.max(dim=1).indices  # (B, embedding dim): winning slot per dim\n",
-    "    pooled[name] = torch.where(vm.unsqueeze(1), masked.max(dim=1).values, torch.zeros_like(masked.max(dim=1).values))\n",
-    "\n",
-    "for name in (\"ego\", \"conditioning\"):\n",
-    "    if name in enc_names:\n",
-    "        pooled[name] = captured[name]\n",
-    "\n",
-    "print(\"\\nCaptured embeddings for:\", enc_names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "01446b9c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ── What survives the max-pool: winning slots, specialization, spatial ──\n",
-    "n = len(set_encs)\n",
-    "fig, axes = plt.subplots(n, 3, figsize=(18, 4.2 * n))\n",
-    "if n == 1:\n",
-    "    axes = axes[None, :]\n",
-    "\n",
-    "print(f\"{'encoder':>9s} | {'valid%':>6s} | {'mean active slots/dim':>21s} | {'%slot-specialized dims':>22s}\")\n",
-    "print(\"-\" * 70)\n",
-    "for r, name in enumerate(set_encs):\n",
-    "    s, e, ns, nf = sl[name]\n",
-    "    vm = valid_sample[name]\n",
-    "    w = winners[name][vm]  # (Bv, D)\n",
-    "    D = w.shape[1]\n",
-    "\n",
-    "    # (1) which slot wins, pooled over all dims+samples\n",
-    "    slot_counts = torch.bincount(w.reshape(-1), minlength=ns).float().cpu().numpy()\n",
-    "    slot_counts = slot_counts / max(slot_counts.sum(), 1)\n",
-    "    axes[r, 0].bar(range(ns), slot_counts, color=\"teal\", alpha=0.85, edgecolor=\"black\")\n",
-    "    axes[r, 0].set_title(f\"{name}: max-pool winner by slot\")\n",
-    "    axes[r, 0].set_xlabel(\"slot index (0 = first/closest)\")\n",
-    "    axes[r, 0].set_ylabel(\"frac of dims won\")\n",
-    "\n",
-    "    # (2) per-dim winner entropy: slot-specialized (0) vs spread across slots (1)\n",
-    "    onehot = F.one_hot(w, num_classes=ns).float()  # (Bv, D, ns)\n",
-    "    p = onehot.mean(dim=0)  # (D, ns) winner distribution per dim\n",
-    "    ent = (-(p * (p + 1e-9).log()).sum(dim=1) / np.log(ns)).cpu().numpy()\n",
-    "    axes[r, 1].hist(ent, bins=30, color=\"indianred\", alpha=0.85, edgecolor=\"black\")\n",
-    "    axes[r, 1].set_title(f\"{name}: per-dim winner entropy\")\n",
-    "    axes[r, 1].set_xlabel(\"0 = slot-specialized   →   1 = spread\")\n",
-    "    axes[r, 1].set_xlim(0, 1)\n",
-    "\n",
-    "    # (3) ego-frame position of the dominant object (mode winning slot per sample)\n",
-    "    dom = torch.mode(w, dim=1).values  # (Bv,)\n",
-    "    rel = raw[name][vm]\n",
-    "    dom_xy = rel[torch.arange(rel.shape[0]), dom][:, :2].cpu().numpy()\n",
-    "    axes[r, 2].scatter(dom_xy[:, 0], dom_xy[:, 1], s=3, alpha=0.15, color=\"navy\")\n",
-    "    axes[r, 2].scatter(0, 0, marker=\"*\", s=200, color=\"red\", zorder=5, label=\"ego\")\n",
-    "    axes[r, 2].set_title(f\"{name}: dominant object position (ego frame)\")\n",
-    "    axes[r, 2].set_xlabel(\"rel_x\")\n",
-    "    axes[r, 2].set_ylabel(\"rel_y\")\n",
-    "    axes[r, 2].set_aspect(\"equal\")\n",
-    "    axes[r, 2].legend(fontsize=8)\n",
-    "\n",
-    "    active_per_dim = np.exp(ent * np.log(ns)).mean()\n",
-    "    print(\n",
-    "        f\"{name:>9s} | {100 * vm.float().mean().item():>5.1f}% | {active_per_dim:>21.2f} | {100 * (ent < 0.2).mean():>21.1f}%\"\n",
-    "    )\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "# ── H1/H2/H3 check: boundary max-pool winner distance vs slot index ──\n",
-    "if \"boundary\" in set_encs:\n",
-    "    vm = valid_sample[\"boundary\"]\n",
-    "    w = winners[\"boundary\"][vm]  # (Bv, D) winning slot per dim\n",
-    "    rb = raw[\"boundary\"][vm]  # (Bv, ns, nf) raw segments\n",
-    "    nsb = rb.shape[1]\n",
-    "    reldist = torch.hypot(rb[:, :, 0], rb[:, :, 1])  # (Bv, ns) normalized ego-frame dist\n",
-    "    valid_seg = ~pad[\"boundary\"][vm]  # (Bv, ns) slots holding a real segment\n",
-    "\n",
-    "    win_reldist = torch.gather(reldist, 1, w)  # (Bv, D) dist of each winning segment\n",
-    "    slot_flat = w.reshape(-1)\n",
-    "    wdist_flat = win_reldist.reshape(-1)\n",
-    "    total_wins = slot_flat.numel()\n",
-    "\n",
-    "    print(\"\\n=== Boundary winner distance vs slot index (H1/H2/H3) ===\")\n",
-    "    print(f\"{'slot':>4s} | {'#wins':>8s} | {'win%':>6s} | {'rel_dist winners':>16s} | {'rel_dist occupied':>17s}\")\n",
-    "    print(\"-\" * 64)\n",
-    "    for s in range(nsb):\n",
-    "        wm = slot_flat == s\n",
-    "        nwin = int(wm.sum())\n",
-    "        wmean = wdist_flat[wm].mean().item() if nwin > 0 else float(\"nan\")\n",
-    "        occ = valid_seg[:, s]\n",
-    "        omean = reldist[occ, s].mean().item() if int(occ.sum()) > 0 else float(\"nan\")\n",
-    "        print(f\"{s:>4d} | {nwin:>8d} | {100 * nwin / total_wins:>5.1f}% | {wmean:>16.4f} | {omean:>17.4f}\")\n",
-    "\n",
-    "    win_mean = wdist_flat.mean().item()\n",
-    "    seg_mean = reldist[valid_seg].mean().item()\n",
-    "    print(f\"\\nMean rel_dist of WINNING segments : {win_mean:.4f}\")\n",
-    "    print(f\"Mean rel_dist of ALL valid segments: {seg_mean:.4f}\")\n",
-    "    verdict = \"FARTHER than avg (H3 supported)\" if win_mean > seg_mean else \"nearer than avg\"\n",
-    "    print(f\"-> winners are {verdict} by {win_mean - seg_mean:+.4f} (normalized units)\")\n",
-    "\n",
-    "    fig, ax = plt.subplots(1, 2, figsize=(14, 4))\n",
-    "    occ_means = [\n",
-    "        reldist[valid_seg[:, s], s].mean().item() if int(valid_seg[:, s].sum()) > 0 else np.nan for s in range(nsb)\n",
-    "    ]\n",
-    "    win_means = [\n",
-    "        wdist_flat[slot_flat == s].mean().item() if int((slot_flat == s).sum()) > 0 else np.nan for s in range(nsb)\n",
-    "    ]\n",
-    "    ax[0].plot(range(nsb), occ_means, \"o-\", label=\"occupied (any segment in slot)\")\n",
-    "    ax[0].plot(range(nsb), win_means, \"s-\", label=\"winners only\")\n",
-    "    ax[0].axhline(seg_mean, color=\"gray\", ls=\"--\", label=\"global valid mean\")\n",
-    "    ax[0].set_xlabel(\"slot index\")\n",
-    "    ax[0].set_ylabel(\"mean rel_dist (normalized)\")\n",
-    "    ax[0].set_title(\"Boundary rel_dist vs slot index\\n(flat = not distance-sorted -> H1/H2)\")\n",
-    "    ax[0].legend(fontsize=8)\n",
-    "    ax[1].hist(reldist[valid_seg].cpu().numpy(), bins=50, alpha=0.6, density=True, label=\"all valid segs\", color=\"gray\")\n",
-    "    ax[1].hist(wdist_flat.cpu().numpy(), bins=50, alpha=0.6, density=True, label=\"winners\", color=\"crimson\")\n",
-    "    ax[1].axvline(seg_mean, color=\"gray\", ls=\"--\")\n",
-    "    ax[1].axvline(win_mean, color=\"crimson\", ls=\"--\")\n",
-    "    ax[1].set_xlabel(\"rel_dist (normalized)\")\n",
-    "    ax[1].set_title(\"Winner vs all-segment distance (H3)\")\n",
-    "    ax[1].legend(fontsize=8)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "44d421ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ── Embedding space: per-encoder contribution, active/dead dims, silence ──\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
-    "\n",
-    "# (1) Mean L2 norm of each pooled embedding = relative weight in the concat fed to backbone\n",
-    "norms = [pooled[n].norm(dim=1).mean().item() for n in enc_names]\n",
-    "axes[0].bar(enc_names, norms, color=\"slateblue\", edgecolor=\"black\")\n",
-    "axes[0].set_title(\"Mean L2 norm of pooled embedding\\n(relative contribution to backbone input)\")\n",
-    "axes[0].tick_params(axis=\"x\", rotation=45)\n",
-    "axes[0].grid(True, axis=\"y\", alpha=0.3)\n",
-    "\n",
-    "# (2) Mean |activation| per embedding dim, per encoder\n",
-    "M = np.stack([pooled[n].abs().mean(0).cpu().numpy() for n in enc_names])\n",
-    "im = axes[1].imshow(M, aspect=\"auto\", cmap=\"magma\")\n",
-    "axes[1].set_yticks(range(len(enc_names)))\n",
-    "axes[1].set_yticklabels(enc_names)\n",
-    "axes[1].set_xlabel(\"embedding dim\")\n",
-    "axes[1].set_title(\"Mean |activation| per embedding dim\")\n",
-    "plt.colorbar(im, ax=axes[1])\n",
-    "\n",
-    "# (3) Dead dims (std<1e-4) — capacity the encoder never uses\n",
-    "dead = [(pooled[n].std(0) < 1e-4).float().mean().item() for n in enc_names]\n",
-    "axes[2].bar(enc_names, dead, color=\"gray\", edgecolor=\"black\")\n",
-    "axes[2].set_title(\"Fraction of dead embedding dims (std < 1e-4)\")\n",
-    "axes[2].tick_params(axis=\"x\", rotation=45)\n",
-    "axes[2].set_ylim(0, 1)\n",
-    "axes[2].grid(True, axis=\"y\", alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "print(f\"{'encoder':>13s} | {'mean|act|':>9s} | {'emb L2':>7s} | {'dead dims':>9s} | {'silence (fully padded)':>22s}\")\n",
-    "print(\"-\" * 80)\n",
-    "for name in enc_names:\n",
-    "    silence = (1 - valid_sample[name].float().mean().item()) if name in valid_sample else 0.0\n",
-    "    deadf = (pooled[name].std(0) < 1e-4).float().mean().item()\n",
-    "    print(\n",
-    "        f\"{name:>13s} | {pooled[name].abs().mean().item():>9.4f} | {pooled[name].norm(dim=1).mean().item():>7.3f} | \"\n",
-    "        f\"{100 * deadf:>7.1f}% | {100 * silence:>21.1f}%\"\n",
-    "    )"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/05_inference.py b/notebooks/05_inference.py
new file mode 100644
index 0000000000..8a2ffc8013
--- /dev/null
+++ b/notebooks/05_inference.py
@@ -0,0 +1,1576 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.3
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # 05 - Model Inference Debug
+# End-to-end inference pipeline: config loading, policy forward pass, rollouts (deterministic vs stochastic), observation/reward analysis, value accuracy, trajectories, LSTM state.
+
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn.functional as F
+from pufferlib.ocean.drive.drive import Drive
+from pufferlib.ocean.drive import binding
+from pufferlib.ocean.torch import Drive as DrivePolicy
+from pufferlib.pytorch import sample_logits
+from notebooks.notebook_utils import COEF_NAMES, EGO_LABELS, MAP_DIR, load_notebook_config, zero_actions
+
+CHECKPOINT_PATH = ""
+ENV_NAME = "puffer_drive"
+
+config = load_notebook_config(CHECKPOINT_PATH, ENV_NAME)
+config["env"]["num_agents"] = 64
+config["env"]["num_maps"] = 8
+config["env"]["eval_mode"] = 1
+config["env"]["map_dir"] = MAP_DIR
+
+config["env"]["obs_slots_boundary_n"] = 80
+config["env"]["obs_slots_lane_n"] = 80
+config["env"]["obs_dropout_lane"] = 0.0
+config["env"]["obs_dropout_boundary"] = 0.0
+
+env = Drive(**config["env"])
+obs, info = env.reset(seed=42)
+N = env.num_agents
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+policy = DrivePolicy(env, **config["policy"]).to(device)
+
+if CHECKPOINT_PATH:
+    sd = torch.load(CHECKPOINT_PATH, map_location=device)
+    sd = {k.replace("module.", ""): v for k, v in sd.items()}
+    policy.load_state_dict(sd)
+    print(f"Loaded checkpoint: {CHECKPOINT_PATH}")
+
+is_continuous = policy.is_continuous
+ACT_SHAPE = (N, len(env.single_action_space.nvec)) if not is_continuous else (N, env.single_action_space.shape[0])
+
+print(f"Policy on {device}, params: {sum(p.numel() for p in policy.parameters()):,}")
+print(f"Obs shape: {obs.shape}, Action space: {env.single_action_space}")
+print(f"Config: dynamics={config['env']['dynamics_model']}, action={config['env']['action_type']}")
+
+# %% [markdown]
+# ## Single-step policy output
+
+# %%
+# Take one step to get fresh obs
+actions = zero_actions(env)
+obs, rew, term, trunc, info = env.step(actions)
+
+obs_tensor = torch.FloatTensor(obs).to(device)
+policy.eval()
+
+with torch.no_grad():
+    logits_list, value = policy(obs_tensor)
+
+# Sample actions
+action, logprob, ent = sample_logits(logits_list)
+action_det, _, _ = sample_logits(logits_list, deterministic=True)
+
+print(f"Value: mean={value.mean():.4f}, std={value.std():.4f}, range=[{value.min():.4f}, {value.max():.4f}]")
+print(f"Entropy: mean={ent.mean():.4f}, std={ent.std():.4f}")
+print(f"LogProb: mean={logprob.mean():.4f}, std={logprob.std():.4f}")
+print(f"Stochastic action sample: {action[0].cpu().numpy()}")
+print(f"Deterministic action: {action_det[0].cpu().numpy()}")
+
+# Plot
+fig, axes = plt.subplots(1, 2, figsize=(14, 4))
+
+# Action probs (first head for multi-discrete, or full logits)
+if isinstance(logits_list, list) or isinstance(logits_list, tuple):
+    probs = F.softmax(logits_list[0], dim=-1)
+else:
+    probs = F.softmax(logits_list, dim=-1)
+mean_probs = probs.mean(dim=0).cpu().numpy()
+axes[0].bar(range(len(mean_probs)), mean_probs, edgecolor="black", alpha=0.7)
+axes[0].axhline(1.0 / len(mean_probs), color="red", ls="--", label="uniform")
+axes[0].set_xlabel("Action")
+axes[0].set_ylabel("Probability")
+axes[0].set_title("Mean action probabilities (across agents)")
+axes[0].legend()
+
+axes[1].hist(value.cpu().numpy().flatten(), bins=30, edgecolor="black", alpha=0.7, color="purple")
+axes[1].set_title("Value predictions across agents")
+axes[1].set_xlabel("Value")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Full rollout: deterministic vs stochastic
+
+# %%
+HORIZON = 256
+TRACKED_AGENT = 0  # agent index to track in detail
+obs_dim = obs.shape[1]
+
+dyn_model = config["env"]["dynamics_model"]
+tgt_type = config["env"]["target_type"]
+rew_cond = config["env"].get("reward_conditioning", False)
+n_tgt_wp = config["env"].get("num_target_waypoints", 3)
+
+
+def run_rollout(env, policy, deterministic=False, horizon=HORIZON):
+    obs, _ = env.reset(seed=42)
+    N = env.num_agents
+
+    buffers = {
+        "obs": np.zeros((horizon, N, obs_dim), dtype=np.float32),
+        "actions": np.zeros((horizon, N), dtype=np.int64),
+        "rewards": np.zeros((horizon, N), dtype=np.float32),
+        "values": np.zeros((horizon, N), dtype=np.float32),
+        "logprobs": np.zeros((horizon, N), dtype=np.float32),
+        "entropy": np.zeros((horizon, N), dtype=np.float32),
+        "terminals": np.zeros((horizon, N), dtype=np.float32),
+        "truncations": np.zeros((horizon, N), dtype=np.float32),
+        "positions_x": np.zeros((horizon, N), dtype=np.float32),
+        "positions_y": np.zeros((horizon, N), dtype=np.float32),
+    }
+
+    policy.eval()
+    for t in range(horizon):
+        obs_t = torch.FloatTensor(obs).to(device)
+        with torch.no_grad():
+            logits_list, val = policy(obs_t)
+            act, logp, entr = sample_logits(logits_list, deterministic=deterministic)
+
+        buffers["obs"][t] = obs
+        buffers["actions"][t] = act.cpu().numpy().reshape(N) if act.dim() > 1 else act.cpu().numpy()
+        buffers["values"][t] = val.squeeze().cpu().numpy()
+        buffers["logprobs"][t] = logp.cpu().numpy()
+        buffers["entropy"][t] = entr.cpu().numpy()
+
+        # Get positions
+        gstate = env.get_global_agent_state()
+        buffers["positions_x"][t] = gstate["x"]
+        buffers["positions_y"][t] = gstate["y"]
+
+        # Step env
+        env_actions = act.cpu().numpy().reshape(ACT_SHAPE)
+        obs, rew, term, trunc, info = env.step(env_actions)
+        buffers["rewards"][t] = rew
+        buffers["terminals"][t] = term
+        buffers["truncations"][t] = trunc
+
+    return buffers
+
+
+print("Running stochastic rollout...")
+buf_stoch = run_rollout(env, policy, deterministic=False)
+print("Running deterministic rollout...")
+buf_det = run_rollout(env, policy, deterministic=True)
+
+for name, buf in [("Stochastic", buf_stoch), ("Deterministic", buf_det)]:
+    print(f"\n--- {name} ---")
+    print(f"  Reward: mean={buf['rewards'].mean():.5f}, std={buf['rewards'].std():.5f}")
+    print(f"  Value: mean={buf['values'].mean():.5f}, std={buf['values'].std():.5f}")
+    print(f"  Entropy: mean={buf['entropy'].mean():.4f}")
+    print(f"  Terminals: {buf['terminals'].sum():.0f}, Truncations: {buf['truncations'].sum():.0f}")
+
+# %% [markdown]
+# ## Observation analysis
+
+# %%
+from pufferlib.viz import unpack_obs, plot_observation, plot_simulator_state
+
+# Ego-centric observation at t=50 for tracked agent
+sample_t = min(50, HORIZON - 1)
+sample_obs = buf_stoch["obs"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]
+print(dyn_model, tgt_type, rew_cond, n_tgt_wp)
+img = plot_observation(
+    sample_obs,
+    target_type=tgt_type,
+    reward_conditioning=rew_cond,
+    num_target_waypoints=n_tgt_wp,
+    obs_slots_partners_n=env.obs_slots_partners_n,
+    obs_slots_lane_n=env.obs_slots_lane_kept,
+    obs_slots_boundary_n=env.obs_slots_boundary_kept,
+    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+    obs_norm_goal_offset_m=env.obs_norm_goal_offset_m,
+    obs_norm_xy_offset_m=env.obs_norm_xy_offset_m,
+    obs_norm_veh_width_m=env.obs_norm_veh_width_m,
+    obs_norm_veh_length_m=env.obs_norm_veh_length_m,
+)
+plt.figure(figsize=(10, 10))
+plt.imshow(img)
+plt.axis("off")
+plt.title(f"Ego-centric obs | agent={TRACKED_AGENT}, t={sample_t}")
+plt.show()
+
+# BEV simulator state
+scenarios = env.get_state()
+if scenarios and len(scenarios) > 0:
+    img_bev = plot_simulator_state(scenarios[0], timestep=0)
+    plt.figure(figsize=(10, 10))
+    plt.imshow(img_bev)
+    plt.axis("off")
+    plt.title("BEV Simulator State")
+    plt.show()
+
+# Ego feature time series for tracked agent
+ego_features_over_time = []
+for t in range(HORIZON):
+    ego, *_ = unpack_obs(
+        buf_stoch["obs"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0],
+        target_type=tgt_type,
+        reward_conditioning=rew_cond,
+        num_target_waypoints=n_tgt_wp,
+        obs_slots_partners_n=env.obs_slots_partners_n,
+        obs_slots_lane_n=env.obs_slots_lane_kept,
+        obs_slots_boundary_n=env.obs_slots_boundary_kept,
+        obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+    )
+    ego_features_over_time.append(ego)
+ego_ts = np.array(ego_features_over_time)
+
+if dyn_model == "jerk":
+    labels = ["speed", "width", "length", "steering", "a_long", "a_lat", "lcenter", "lalign", "speed_limit"]
+    plot_idxs = [0, 3, 4, 5]  # speed, steering, a_long, a_lat
+else:
+    labels = ["speed", "width", "length", "lcenter", "lalign", "speed_limit"]
+    plot_idxs = [0, 3, 4, 5]  # speed, lcenter, lalign, speed_limit
+
+fig, axes = plt.subplots(len(plot_idxs), 1, figsize=(14, 3 * len(plot_idxs)), sharex=True)
+for i, idx in enumerate(plot_idxs):
+    axes[i].plot(ego_ts[:, idx])
+    print(ego_ts[10:, idx].argmin())
+    axes[i].set_ylabel(labels[idx])
+    axes[i].grid(True, alpha=0.3)
+axes[-1].set_xlabel("Step")
+fig.suptitle(f"Ego features over time | agent={TRACKED_AGENT}", fontsize=14)
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Observation layer breakdown
+#
+# Obs layout (all ego-centric, normalized):
+# - **Ego**: speed, width, length, [jerk: steering, a_long, a_lat], lane_center_dist, lane_angle, speed_limit
+# - **Conditioning** (if enabled): 17 reward coefs (goal_radius, goal_speed, collision, offroad, comfort, lane_align, vel_align, lane_center, center_bias, velocity, reverse, stop_line, timestep, overspeed, throttle, steer, acc) + target waypoints
+# - **Target**: static=rel_x,rel_y,rel_z per waypoint; dynamic=rel_x,rel_y,rel_z,heading_cos,heading_sin per waypoint
+# - **Partners** (MAX_PARTNERS x 9): rel_x, rel_y, rel_z, length, width, heading_cos, heading_sin, rel_vx, rel_vy, seconds_stopped
+# - **Lanes** (MAX_LANES x 7): rel_x, rel_y, rel_z, seg_length, seg_width, dir_cos, dir_sin
+# - **Boundaries** (MAX_BOUNDS x 7): same as lanes
+# - **Traffic controls** (MAX_TRAFFIC x 7): rel_x1, rel_y1, rel_x2, rel_y2, rel_z, type, state
+
+# %%
+from pufferlib.viz import unpack_obs
+
+sample_t = min(50, HORIZON - 1)
+sample_obs = buf_stoch["obs"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]
+ego, target, partners, lanes, boundaries, traffic_controls = unpack_obs(
+    sample_obs,
+    target_type=tgt_type,
+    reward_conditioning=rew_cond,
+    num_target_waypoints=n_tgt_wp,
+    obs_slots_partners_n=env.obs_slots_partners_n,
+    obs_slots_lane_n=env.obs_slots_lane_kept,
+    obs_slots_boundary_n=env.obs_slots_boundary_kept,
+    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+)
+
+# Also unpack conditioning manually (unpack_obs doesn't return it separately)
+ego_dim = binding.EGO_FEATURES
+cond_dim = binding.NUM_REWARD_COEFS if rew_cond else 0
+cond_obs = sample_obs[0, ego_dim : ego_dim + cond_dim] if cond_dim > 0 else None
+
+
+# --- Print all layer shapes + stats ---
+def layer_stats(name, arr):
+    flat = arr.flatten() if hasattr(arr, "flatten") else np.array(arr).flatten()
+    if flat.size == 0:
+        print(f"{name:>14s}: shape={str(list(arr.shape)):>16s}  (empty)")
+        return
+    nonzero = np.count_nonzero(flat)
+    print(
+        f"{name:>14s}: shape={str(list(arr.shape)):>16s}  "
+        f"nonzero={nonzero:>5d}/{flat.size:<5d}  "
+        f"range=[{flat.min():.4f}, {flat.max():.4f}]  "
+        f"mean={flat.mean():.4f}  std={flat.std():.4f}"
+    )
+
+
+print(f"--- Observation breakdown at t={sample_t}, agent={TRACKED_AGENT} ---")
+print(f"Total obs dim: {sample_obs.shape[-1]}")
+print()
+layer_stats("Ego", ego)
+if cond_obs is not None:
+    layer_stats("Conditioning", cond_obs)
+layer_stats("Target", target)
+layer_stats("Partners", partners)
+layer_stats("Lanes", lanes)
+layer_stats("Boundaries", boundaries)
+layer_stats("TrafficControls", traffic_controls)
+
+# --- Ego features detail ---
+ego_labels = EGO_LABELS
+
+print(f"\n--- Ego features ---")
+for i, (label, val) in enumerate(zip(ego_labels, ego)):
+    print(f"  [{i}] {label:>14s} = {val:.4f}")
+
+# --- Conditioning detail ---
+if cond_obs is not None:
+    cond_labels = COEF_NAMES
+    print(f"\n--- Conditioning (reward coefs, normalized) ---")
+    for i, (label, val) in enumerate(zip(cond_labels, cond_obs)):
+        print(f"  [{i:>2d}] {label:>16s} = {val:.4f}")
+
+# --- Target waypoints ---
+tgt_feat = binding.STATIC_TARGET_FEATURES if tgt_type == "static" else binding.DYNAMIC_TARGET_FEATURES
+if tgt_type == "static":
+    tgt_labels = ["rel_x", "rel_y", "rel_z"]
+else:
+    tgt_labels = ["rel_x", "rel_y", "rel_z", "heading_cos", "heading_sin"]
+
+print(f"\n--- Target waypoints (n={n_tgt_wp}, type={tgt_type}) ---")
+for wp in range(target.shape[0]):
+    vals = ", ".join(f"{tgt_labels[j]}={target[wp, j]:.4f}" for j in range(tgt_feat))
+    active = "ACTIVE" if not np.allclose(target[wp], 0) else "zeroed"
+    print(f"  wp[{wp}]: {vals}  ({active})")
+
+# --- Partner summary ---
+n_visible = np.sum(np.any(partners != 0, axis=1))
+print(f"\n--- Partners: {n_visible}/{partners.shape[0]} visible ---")
+partner_labels = [
+    "rel_x",
+    "rel_y",
+    "rel_z",
+    "length",
+    "width",
+    "heading_cos",
+    "heading_sin",
+    "rel_vx",
+    "rel_vy",
+    "seconds_stopped",
+]
+for p in range(min(int(n_visible), 5)):
+    vals = ", ".join(f"{partner_labels[j]}={partners[p, j]:.3f}" for j in range(env.partner_features))
+    print(f"  [{p}] {vals}")
+if n_visible > 5:
+    print(f"  ... ({n_visible - 5} more)")
+
+# --- Lane/boundary occupancy ---
+n_lanes = np.sum(np.any(lanes != 0, axis=1))
+n_bounds = np.sum(np.any(boundaries != 0, axis=1))
+print(f"\n--- Road: {n_lanes}/{lanes.shape[0]} lane segs, {n_bounds}/{boundaries.shape[0]} boundary segs ---")
+
+# --- Traffic ---
+n_traffic = np.sum(np.any(traffic_controls != 0, axis=1))
+print(f"\n--- Traffic controls: {n_traffic}/{traffic_controls.shape[0]} visible ---")
+traffic_labels = ["rel_x1", "rel_y1", "rel_x2", "rel_y2", "rel_z", "type", "state"]
+for t in range(min(int(n_traffic), 5)):
+    vals = ", ".join(
+        f"{traffic_labels[j]}={traffic_controls[t, j]:.3f}"
+        for j in range(min(len(traffic_labels), traffic_controls.shape[1]))
+    )
+    print(f"  [{t}] {vals}")
+
+# %%
+# --- Layer-level stats across ALL agents at sample_t ---
+all_obs = buf_stoch["obs"][sample_t]  # (N, obs_dim)
+
+ego_dim = binding.EGO_FEATURES
+cond_dim = binding.NUM_REWARD_COEFS if rew_cond else 0
+tgt_feat = binding.STATIC_TARGET_FEATURES if tgt_type == "static" else binding.DYNAMIC_TARGET_FEATURES
+tgt_dim = n_tgt_wp * tgt_feat
+partner_dim = env.obs_slots_partners_n * env.partner_features
+lane_dim = env.obs_slots_lane_kept * env.road_features
+boundary_dim = env.obs_slots_boundary_kept * env.road_features
+traffic_dim = env.obs_slots_traffic_controls_n * env.traffic_control_features
+
+# Slice indices
+idx = 0
+slices = {}
+slices["ego"] = (idx, idx + ego_dim)
+idx += ego_dim
+if cond_dim > 0:
+    slices["conditioning"] = (idx, idx + cond_dim)
+    idx += cond_dim
+slices["target"] = (idx, idx + tgt_dim)
+idx += tgt_dim
+slices["partners"] = (idx, idx + partner_dim)
+idx += partner_dim
+slices["lanes"] = (idx, idx + lane_dim)
+idx += lane_dim
+slices["boundaries"] = (idx, idx + boundary_dim)
+idx += boundary_dim
+slices["traffic"] = (idx, idx + traffic_dim)
+idx += traffic_dim
+
+print(f"Obs dim used: {idx} / {all_obs.shape[1]}")
+print(
+    f"\n{'Layer':>14s} | {'start':>5s}-{'end':>5s} | {'dim':>5s} | {'mean':>8s} | {'std':>8s} | {'min':>8s} | {'max':>8s} | {'%nonzero':>8s}"
+)
+print("-" * 95)
+for name, (s, e) in slices.items():
+    chunk = all_obs[:, s:e]
+    nz_pct = 100 * np.count_nonzero(chunk) / chunk.size
+    print(
+        f"{name:>14s} | {s:>5d}-{e:>5d} | {e - s:>5d} | {chunk.mean():>8.4f} | {chunk.std():>8.4f} | "
+        f"{chunk.min():>8.4f} | {chunk.max():>8.4f} | {nz_pct:>7.1f}%"
+    )
+
+# --- Plots ---
+n_layers = len(slices)
+fig, axes = plt.subplots(2, (n_layers + 1) // 2, figsize=(5 * ((n_layers + 1) // 2), 8))
+axes = axes.flatten()
+
+for i, (name, (s, e)) in enumerate(slices.items()):
+    chunk = all_obs[:, s:e].flatten()
+    # Filter out exact zeros for histogram readability on sparse layers
+    nonzero_vals = chunk[chunk != 0]
+    if len(nonzero_vals) > 0:
+        axes[i].hist(nonzero_vals, bins=50, edgecolor="black", alpha=0.7)
+        axes[i].set_title(f"{name} (nonzero only, {len(nonzero_vals)}/{len(chunk)})")
+    else:
+        axes[i].hist(chunk, bins=50, edgecolor="black", alpha=0.7)
+        axes[i].set_title(f"{name} (all zeros)")
+    axes[i].set_xlabel("Value")
+
+# Hide unused axes
+for j in range(i + 1, len(axes)):
+    axes[j].set_visible(False)
+
+fig.suptitle(f"Observation distributions across {N} agents at t={sample_t}", fontsize=14)
+plt.tight_layout()
+plt.show()
+
+# %%
+# --- Per-feature detail for partners, lanes, boundaries over time (tracked agent) ---
+
+
+def unpack_all_timesteps(bufs, agent_idx):
+    """Unpack all obs layers across time for one agent."""
+    H = bufs["obs"].shape[0]
+    egos, targets, conds = [], [], []
+    n_partners, n_lanes, n_bounds, n_traffic = [], [], [], []
+
+    for t in range(H):
+        ob = bufs["obs"][t : t + 1, agent_idx : agent_idx + 1][0]
+        ego, tgt, part, lane, bnd, tfc = unpack_obs(
+            ob,
+            target_type=tgt_type,
+            reward_conditioning=rew_cond,
+            num_target_waypoints=n_tgt_wp,
+            obs_slots_partners_n=env.obs_slots_partners_n,
+            obs_slots_lane_n=env.obs_slots_lane_kept,
+            obs_slots_boundary_n=env.obs_slots_boundary_kept,
+            obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+        )
+        egos.append(ego)
+        targets.append(tgt)
+        n_partners.append(np.sum(np.any(part != 0, axis=1)))
+        n_lanes.append(np.sum(np.any(lane != 0, axis=1)))
+        n_bounds.append(np.sum(np.any(bnd != 0, axis=1)))
+        n_traffic.append(np.sum(np.any(tfc != 0, axis=1)))
+
+        if rew_cond:
+            ed = binding.EGO_FEATURES
+            conds.append(ob[0, ed : ed + binding.NUM_REWARD_COEFS])
+
+    return {
+        "ego": np.array(egos),
+        "target": np.array(targets),
+        "cond": np.array(conds) if conds else None,
+        "n_partners": np.array(n_partners),
+        "n_lanes": np.array(n_lanes),
+        "n_bounds": np.array(n_bounds),
+        "n_traffic": np.array(n_traffic),
+    }
+
+
+ts = unpack_all_timesteps(buf_stoch, TRACKED_AGENT)
+
+fig, axes = plt.subplots(2, 2, figsize=(16, 10))
+
+# Occupancy over time
+axes[0, 0].plot(ts["n_partners"], label="partners", alpha=0.8)
+axes[0, 0].plot(ts["n_lanes"], label="lanes", alpha=0.8)
+axes[0, 0].plot(ts["n_bounds"], label="boundaries", alpha=0.8)
+axes[0, 0].plot(ts["n_traffic"], label="traffic", alpha=0.8)
+axes[0, 0].set_xlabel("Step")
+axes[0, 0].set_ylabel("Visible count")
+axes[0, 0].set_title(f"Obs occupancy over time | agent={TRACKED_AGENT}")
+axes[0, 0].legend()
+axes[0, 0].grid(True, alpha=0.3)
+
+# Target waypoint distances over time
+tgt_x = ts["target"][:, :, 0]
+tgt_y = ts["target"][:, :, 1]
+tgt_dist = np.sqrt(tgt_x**2 + tgt_y**2)
+for wp in range(n_tgt_wp):
+    axes[0, 1].plot(tgt_dist[:, wp], label=f"wp[{wp}]", alpha=0.8)
+axes[0, 1].set_xlabel("Step")
+axes[0, 1].set_ylabel("Distance (normalized)")
+axes[0, 1].set_title("Target waypoint distance over time")
+axes[0, 1].legend()
+axes[0, 1].grid(True, alpha=0.3)
+
+# Conditioning heatmap over time
+if ts["cond"] is not None:
+    cond_labels = COEF_NAMES
+    im = axes[1, 0].imshow(ts["cond"].T, aspect="auto", cmap="coolwarm", interpolation="nearest")
+    axes[1, 0].set_yticks(range(len(cond_labels)))
+    axes[1, 0].set_yticklabels(cond_labels, fontsize=8)
+    axes[1, 0].set_xlabel("Step")
+    axes[1, 0].set_title("Conditioning coefs over time")
+    plt.colorbar(im, ax=axes[1, 0])
+else:
+    axes[1, 0].text(0.5, 0.5, "No conditioning", ha="center", va="center", transform=axes[1, 0].transAxes)
+    axes[1, 0].set_title("Conditioning (disabled)")
+
+# Partner closest distance over time
+partner_dists = []
+for t in range(HORIZON):
+    ob = buf_stoch["obs"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]
+    _, _, part, _, _, _ = unpack_obs(
+        ob,
+        target_type=tgt_type,
+        reward_conditioning=rew_cond,
+        num_target_waypoints=n_tgt_wp,
+        obs_slots_partners_n=env.obs_slots_partners_n,
+        obs_slots_lane_n=env.obs_slots_lane_kept,
+        obs_slots_boundary_n=env.obs_slots_boundary_kept,
+        obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+    )
+    dists = np.sqrt(part[:, 0] ** 2 + part[:, 1] ** 2)
+    visible = np.any(part != 0, axis=1)
+    partner_dists.append(dists[visible].min() if visible.any() else np.nan)
+
+axes[1, 1].plot(partner_dists, alpha=0.8, color="red")
+axes[1, 1].set_xlabel("Step")
+axes[1, 1].set_ylabel("Min partner dist (normalized)")
+axes[1, 1].set_title("Closest partner distance over time")
+axes[1, 1].grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+
+# %%
+# --- Spatial scatter: all observed entities in ego frame at sample_t ---
+sample_obs = buf_stoch["obs"][sample_t : sample_t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]
+ego, target, partners, lanes, boundaries, traffic_controls = unpack_obs(
+    sample_obs,
+    target_type=tgt_type,
+    reward_conditioning=rew_cond,
+    num_target_waypoints=n_tgt_wp,
+    obs_slots_partners_n=env.obs_slots_partners_n,
+    obs_slots_lane_n=env.obs_slots_lane_kept,
+    obs_slots_boundary_n=env.obs_slots_boundary_kept,
+    obs_slots_traffic_controls_n=env.obs_slots_traffic_controls_n,
+)
+
+fig, ax = plt.subplots(figsize=(10, 10))
+
+# Ego vehicle at origin
+from matplotlib.patches import Rectangle
+
+ax.add_patch(
+    Rectangle((-ego[2] / 2, -ego[1] / 2), ego[2], ego[1], facecolor="blue", edgecolor="black", alpha=0.7, zorder=10)
+)
+ax.annotate("EGO", (0, 0), fontsize=9, ha="center", va="center", color="white", fontweight="bold", zorder=11)
+
+# Lane segments
+for i in range(lanes.shape[0]):
+    if np.allclose(lanes[i], 0):
+        continue
+    rx, ry, rz, length, _, dc, ds = lanes[i]
+    ax.plot(
+        [rx - dc * length / 2, rx + dc * length / 2],
+        [ry - ds * length / 2, ry + ds * length / 2],
+        color="lightgray",
+        linewidth=1,
+        zorder=1,
+    )
+ax.scatter(
+    lanes[np.any(lanes != 0, axis=1), 0],
+    lanes[np.any(lanes != 0, axis=1), 1],
+    s=5,
+    color="gray",
+    alpha=0.5,
+    label=f"lanes ({n_lanes})",
+    zorder=2,
+)
+
+# Boundary segments
+for i in range(boundaries.shape[0]):
+    if np.allclose(boundaries[i], 0):
+        continue
+    rx, ry, rz, length, _, dc, ds = boundaries[i]
+    ax.plot(
+        [rx - dc * length / 2, rx + dc * length / 2],
+        [ry - ds * length / 2, ry + ds * length / 2],
+        color="black",
+        linewidth=1,
+        zorder=1,
+    )
+bnd_mask = np.any(boundaries != 0, axis=1)
+if bnd_mask.any():
+    ax.scatter(
+        boundaries[bnd_mask, 0],
+        boundaries[bnd_mask, 1],
+        s=8,
+        color="black",
+        alpha=0.6,
+        label=f"boundaries ({n_bounds})",
+        zorder=2,
+    )
+
+# Partners
+for i in range(partners.shape[0]):
+    if np.allclose(partners[i], 0):
+        continue
+    rx, ry, rz, w, l, hc, hs, vx, vy, _ = partners[i]
+    heading = np.arctan2(hs, hc)
+    rect = Rectangle((-l / 2, -w / 2), l, w, facecolor="orange", edgecolor="black", alpha=0.6, zorder=9)
+    rect.set_transform(plt.matplotlib.transforms.Affine2D().rotate(heading).translate(rx, ry) + ax.transData)
+    ax.add_patch(rect)
+    ax.annotate(f"{vx:.2f}, {vy:.2f}", (rx, ry), fontsize=7, ha="center", color="darkred", zorder=12)
+part_mask = np.any(partners != 0, axis=1)
+if part_mask.any():
+    ax.scatter(
+        partners[part_mask, 0],
+        partners[part_mask, 1],
+        s=40,
+        color="orange",
+        edgecolors="black",
+        label=f"partners ({n_visible})",
+        zorder=8,
+    )
+
+# Target waypoints
+for wp in range(target.shape[0]):
+    if np.allclose(target[wp], 0):
+        continue
+    marker = "*" if wp == 0 else "o"
+    s = 200 if wp == 0 else 80
+    color = "red" if wp == 0 else "salmon"
+    ax.scatter(
+        target[wp, 0],
+        target[wp, 1],
+        color=color,
+        marker=marker,
+        s=s,
+        zorder=15,
+        label=f"target wp[{wp}]" if wp < 3 else None,
+    )
+
+# Traffic controls
+for i in range(traffic_controls.shape[0]):
+    if np.allclose(traffic_controls[i], 0):
+        continue
+    x1, y1, x2, y2, _, control_type, state = traffic_controls[i]
+    if int(control_type) == binding.TRAFFIC_CONTROL_TYPE_TRAFFIC_LIGHT:
+        state_colors = {
+            binding.TRAFFIC_CONTROL_STATE_UNKNOWN: "gray",
+            binding.TRAFFIC_CONTROL_STATE_RED: "red",
+            binding.TRAFFIC_CONTROL_STATE_YELLOW: "yellow",
+            binding.TRAFFIC_CONTROL_STATE_GREEN: "green",
+            binding.TRAFFIC_CONTROL_STATE_OFF: "gray",
+        }
+        ax.plot([x1, x2], [y1, y2], color=state_colors.get(int(state), "gray"), linewidth=3, zorder=15)
+    else:
+        accent = "red" if int(control_type) == binding.TRAFFIC_CONTROL_TYPE_STOP_SIGN else "gold"
+        ax.plot([x1, x2], [y1, y2], color="black", linewidth=4, zorder=14)
+        ax.plot([x1, x2], [y1, y2], color=accent, linewidth=2.5, linestyle="--", zorder=15)
+
+ax.set_xlim(-1, 1)
+ax.set_ylim(-1, 1)
+ax.set_aspect("equal")
+ax.set_xlabel("X (ego frame, normalized)")
+ax.set_ylabel("Y (ego frame, normalized)")
+ax.set_title(f"All observed entities | agent={TRACKED_AGENT}, t={sample_t}")
+ax.legend(loc="upper right", fontsize=8)
+ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ### Ego + conditioning distributions across all agents
+
+# %%
+# Ego feature distributions across all agents, pooled over full rollout
+ego_dim = binding.EGO_FEATURES
+all_ego = buf_stoch["obs"][:, :, :ego_dim].reshape(-1, ego_dim)  # (H*N, ego_dim)
+
+ego_labels = EGO_LABELS
+
+fig, axes = plt.subplots(2, len(ego_labels), figsize=(3.5 * len(ego_labels), 7))
+
+# Row 0: histograms
+for i, label in enumerate(ego_labels):
+    vals = all_ego[:, i]
+    print(f"{label}: mean={vals}")
+    axes[0, i].hist(vals, bins=60, edgecolor="black", alpha=0.7, color="steelblue")
+    axes[0, i].set_title(label, fontsize=10)
+    axes[0, i].set_xlabel("")
+    axes[0, i].tick_params(labelsize=7)
+    axes[0, i].axvline(vals.mean(), color="red", ls="--", lw=1)
+
+# Row 1: boxplots per-agent (distribution across timesteps for each agent)
+ego_per_agent = buf_stoch["obs"][:, :, :ego_dim]  # (H, N, ego_dim)
+for i, label in enumerate(ego_labels):
+    data = [ego_per_agent[:, a, i] for a in range(N)]
+    bp = axes[1, i].boxplot(
+        data,
+        showfliers=False,
+        patch_artist=True,
+        boxprops=dict(facecolor="steelblue", alpha=0.5),
+        medianprops=dict(color="red"),
+    )
+    axes[1, i].set_xlabel("Agent")
+    axes[1, i].tick_params(labelsize=7)
+    axes[1, i].set_title(f"{label} per agent", fontsize=9)
+
+fig.suptitle("Ego features: full rollout distributions", fontsize=13)
+plt.tight_layout()
+plt.show()
+
+# Conditioning distributions across all agents (if enabled)
+if rew_cond:
+    cond_start = ego_dim
+    cond_end = cond_start + binding.NUM_REWARD_COEFS
+    all_cond = buf_stoch["obs"][:, :, cond_start:cond_end].reshape(-1, binding.NUM_REWARD_COEFS)
+
+    cond_labels = COEF_NAMES
+
+    fig, ax = plt.subplots(figsize=(14, 5))
+    parts = ax.violinplot(
+        [all_cond[:, i] for i in range(binding.NUM_REWARD_COEFS)],
+        positions=range(binding.NUM_REWARD_COEFS),
+        showmeans=True,
+        showmedians=True,
+    )
+    ax.set_xticks(range(binding.NUM_REWARD_COEFS))
+    ax.set_xticklabels(cond_labels, rotation=45, ha="right", fontsize=9)
+    ax.set_ylabel("Normalized value")
+    ax.set_title("Conditioning coef distributions (all agents, full rollout)")
+    ax.grid(True, alpha=0.3, axis="y")
+    plt.tight_layout()
+    plt.show()
+
+# %% [markdown]
+# ### Partner per-feature distributions
+
+# %%
+# Partner per-feature distributions (pooled over all agents + timesteps, visible only)
+partner_labels = [
+    "rel_x",
+    "rel_y",
+    "rel_z",
+    "length",
+    "width",
+    "heading_cos",
+    "heading_sin",
+    "rel_vx",
+    "rel_vy",
+    "seconds_stopped",
+]
+obs_slots_partners_n = env.obs_slots_partners_n
+pf = env.partner_features
+
+# Compute slices
+_ego_d = binding.EGO_FEATURES
+_cond_d = binding.NUM_REWARD_COEFS if rew_cond else 0
+_tgt_f = binding.STATIC_TARGET_FEATURES if tgt_type == "static" else binding.DYNAMIC_TARGET_FEATURES
+_tgt_d = n_tgt_wp * _tgt_f
+_p_start = _ego_d + _cond_d + _tgt_d
+_p_end = _p_start + obs_slots_partners_n * pf
+
+all_partners = buf_stoch["obs"][:, :, _p_start:_p_end].reshape(
+    -1, obs_slots_partners_n, pf
+)  # (H*N, obs_slots_partners_n, 10)
+# Mask: partner is visible if any feature != 0
+visible_mask = np.any(all_partners != 0, axis=2)  # (H*N, 16)
+visible_partners = all_partners[visible_mask]  # (K, 10) — all visible partner observations
+
+print(
+    f"Total partner obs: {all_partners.shape[0] * obs_slots_partners_n}, visible: {len(visible_partners)} "
+    f"({100 * len(visible_partners) / (all_partners.shape[0] * obs_slots_partners_n):.1f}%)"
+)
+
+fig, axes = plt.subplots(2, 5, figsize=(21, 8))
+axes = axes.flatten()
+
+for i, label in enumerate(partner_labels):
+    vals = visible_partners[:, i]
+    axes[i].hist(vals, bins=80, edgecolor="black", alpha=0.7, color="darkorange")
+    axes[i].set_title(f"{label} (n={len(vals)})", fontsize=10)
+    axes[i].axvline(vals.mean(), color="red", ls="--", lw=1, label=f"mean={vals.mean():.3f}")
+    axes[i].legend(fontsize=7)
+    axes[i].tick_params(labelsize=7)
+
+# rel_x vs rel_y scatter in last panel
+pos_ax = axes[len(partner_labels)]
+pos_ax.scatter(visible_partners[:, 0], visible_partners[:, 1], s=1, alpha=0.15, color="darkorange")
+pos_ax.set_xlabel("rel_x")
+pos_ax.set_ylabel("rel_y")
+pos_ax.set_title("Partner positions (ego frame)")
+pos_ax.set_aspect("equal")
+pos_ax.grid(True, alpha=0.3)
+
+fig.suptitle("Partner features: all visible, full rollout", fontsize=13)
+plt.tight_layout()
+plt.show()
+
+# Partner count distribution across (timestep, agent)
+partner_counts = visible_mask.sum(axis=1)  # (H*N,)
+fig, axes = plt.subplots(1, 2, figsize=(12, 4))
+axes[0].hist(partner_counts, bins=range(obs_slots_partners_n + 2), edgecolor="black", alpha=0.7, color="darkorange")
+axes[0].set_xlabel("Visible partners")
+axes[0].set_ylabel("Count")
+axes[0].set_title("Partner count distribution (per agent per step)")
+
+# Partner distance distribution
+dists = np.sqrt(visible_partners[:, 0] ** 2 + visible_partners[:, 1] ** 2)
+axes[1].hist(dists, bins=80, edgecolor="black", alpha=0.7, color="coral")
+axes[1].set_xlabel("Distance (normalized)")
+axes[1].set_ylabel("Count")
+axes[1].set_title(f"Partner distance distribution (mean={dists.mean():.3f})")
+axes[1].axvline(dists.mean(), color="red", ls="--", lw=1)
+
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ### Road (lanes + boundaries) and target distributions
+
+# %%
+# Road per-feature distributions (lanes + boundaries)
+road_labels = ["rel_x", "rel_y", "rel_z", "seg_length", "seg_width", "dir_cos", "dir_sin"]
+rf = env.road_features
+max_lanes = env.obs_slots_lane_kept
+max_bounds = env.obs_slots_boundary_kept
+
+_l_start = _p_end
+_l_end = _l_start + max_lanes * rf
+_b_start = _l_end
+_b_end = _b_start + max_bounds * rf
+
+all_lanes = buf_stoch["obs"][:, :, _l_start:_l_end].reshape(-1, max_lanes, rf)
+all_bounds = buf_stoch["obs"][:, :, _b_start:_b_end].reshape(-1, max_bounds, rf)
+
+vis_lanes = all_lanes[np.any(all_lanes != 0, axis=2)]
+vis_bounds = all_bounds[np.any(all_bounds != 0, axis=2)]
+
+print(
+    f"Lanes: {len(vis_lanes)} visible / {all_lanes.shape[0] * max_lanes} total "
+    f"({100 * len(vis_lanes) / (all_lanes.shape[0] * max_lanes):.1f}%)"
+)
+print(
+    f"Boundaries: {len(vis_bounds)} visible / {all_bounds.shape[0] * max_bounds} total "
+    f"({100 * len(vis_bounds) / (all_bounds.shape[0] * max_bounds):.1f}%)"
+)
+
+fig, axes = plt.subplots(2, 7, figsize=(28, 8))
+for i, label in enumerate(road_labels):
+    # Lanes
+    axes[0, i].hist(vis_lanes[:, i], bins=80, edgecolor="black", alpha=0.7, color="silver")
+    axes[0, i].set_title(f"lane {label}", fontsize=9)
+    axes[0, i].axvline(vis_lanes[:, i].mean(), color="red", ls="--", lw=1)
+    axes[0, i].tick_params(labelsize=7)
+    # Boundaries
+    axes[1, i].hist(vis_bounds[:, i], bins=80, edgecolor="black", alpha=0.7, color="dimgray")
+    axes[1, i].set_title(f"boundary {label}", fontsize=9)
+    axes[1, i].axvline(vis_bounds[:, i].mean(), color="red", ls="--", lw=1)
+    axes[1, i].tick_params(labelsize=7)
+
+fig.suptitle("Road features: all visible, full rollout (top=lanes, bottom=boundaries)", fontsize=13)
+plt.tight_layout()
+plt.show()
+
+# Spatial scatter: lane vs boundary positions (pooled)
+fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+axes[0].scatter(vis_lanes[:, 0], vis_lanes[:, 1], s=0.5, alpha=0.05, color="gray")
+axes[0].set_xlabel("rel_x")
+axes[0].set_ylabel("rel_y")
+axes[0].set_title(f"Lane segment positions (n={len(vis_lanes)})")
+axes[0].set_aspect("equal")
+axes[0].grid(True, alpha=0.3)
+
+axes[1].scatter(vis_bounds[:, 0], vis_bounds[:, 1], s=0.5, alpha=0.05, color="black")
+axes[1].set_xlabel("rel_x")
+axes[1].set_ylabel("rel_y")
+axes[1].set_title(f"Boundary segment positions (n={len(vis_bounds)})")
+axes[1].set_aspect("equal")
+axes[1].grid(True, alpha=0.3)
+
+# Lane + boundary segment length comparison
+axes[2].hist(vis_lanes[:, 2], bins=80, alpha=0.6, color="silver", edgecolor="black", label="lanes")
+axes[2].hist(vis_bounds[:, 2], bins=80, alpha=0.6, color="dimgray", edgecolor="black", label="boundaries")
+axes[2].set_xlabel("Segment length (normalized)")
+axes[2].set_ylabel("Count")
+axes[2].set_title("Segment length distribution")
+axes[2].legend()
+
+plt.tight_layout()
+plt.show()
+
+# Target distributions across all agents, full rollout
+_tgt_start = _ego_d + _cond_d
+_tgt_end = _tgt_start + _tgt_d
+all_target = buf_stoch["obs"][:, :, _tgt_start:_tgt_end].reshape(-1, n_tgt_wp, _tgt_f)
+
+if tgt_type == "static":
+    tgt_flabels = ["rel_x", "rel_y", "rel_z"]
+else:
+    tgt_flabels = ["rel_x", "rel_y", "rel_z", "heading_cos", "heading_sin"]
+
+fig, axes = plt.subplots(1, n_tgt_wp + 1, figsize=(5 * (n_tgt_wp + 1), 4))
+
+for wp in range(n_tgt_wp):
+    wp_data = all_target[:, wp, :]
+    active = np.any(wp_data != 0, axis=1)
+    wp_active = wp_data[active]
+    dist = np.sqrt(wp_active[:, 0] ** 2 + wp_active[:, 1] ** 2) if len(wp_active) > 0 else np.array([])
+    axes[wp].hist(dist, bins=60, edgecolor="black", alpha=0.7, color=["red", "salmon", "lightsalmon"][wp % 3])
+    axes[wp].set_title(f"wp[{wp}] distance (n={len(wp_active)}/{len(wp_data)})", fontsize=10)
+    axes[wp].set_xlabel("Distance (normalized)")
+
+# All waypoints x-y scatter
+for wp in range(n_tgt_wp):
+    wp_data = all_target[:, wp, :]
+    active = np.any(wp_data != 0, axis=1)
+    wp_active = wp_data[active]
+    if len(wp_active) > 0:
+        axes[n_tgt_wp].scatter(wp_active[:, 0], wp_active[:, 1], s=1, alpha=0.1, label=f"wp[{wp}]")
+axes[n_tgt_wp].set_xlabel("rel_x")
+axes[n_tgt_wp].set_ylabel("rel_y")
+axes[n_tgt_wp].set_title("Target positions (ego frame)")
+axes[n_tgt_wp].set_aspect("equal")
+axes[n_tgt_wp].legend(fontsize=8)
+axes[n_tgt_wp].grid(True, alpha=0.3)
+
+fig.suptitle("Target waypoint distributions (all agents, full rollout)", fontsize=13)
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ### Observation sparsity and layer occupancy heatmaps
+
+# %%
+# Sparsity heatmap: fraction of nonzero per layer, per agent, over time
+layer_names = ["partners", "lanes", "boundaries"]
+layer_slices = [
+    (_p_start, _p_end, env.obs_slots_partners_n, env.partner_features),
+    (_l_start, _l_end, env.obs_slots_lane_kept, env.road_features),
+    (_b_start, _b_end, env.obs_slots_boundary_kept, env.road_features),
+]
+
+fig, axes = plt.subplots(1, 3, figsize=(20, 5))
+for ax, name, (s, e, n_obj, n_feat) in zip(axes, layer_names, layer_slices):
+    # (H, N) -> fraction of visible objects per (timestep, agent)
+    raw = buf_stoch["obs"][:, :, s:e].reshape(HORIZON, env.num_agents, n_obj, n_feat)
+    occupancy = np.any(raw != 0, axis=3).sum(axis=2) / n_obj  # (H, N)
+    im = ax.imshow(occupancy.T, aspect="auto", cmap="YlOrRd", interpolation="nearest", vmin=0, vmax=1)
+    ax.set_xlabel("Step")
+    ax.set_ylabel("Agent")
+    ax.set_title(f"{name} occupancy (frac visible)")
+    plt.colorbar(im, ax=ax)
+
+plt.suptitle("Per-layer occupancy heatmaps (fraction of max slots filled)", fontsize=13)
+plt.tight_layout()
+plt.show()
+
+# Per-layer mean occupancy over time
+fig, axes = plt.subplots(1, 2, figsize=(16, 4))
+
+# Mean across agents
+for name, (s, e, n_obj, n_feat) in zip(layer_names, layer_slices):
+    raw = buf_stoch["obs"][:, :, s:e].reshape(HORIZON, env.num_agents, n_obj, n_feat)
+    occ_mean = np.any(raw != 0, axis=3).sum(axis=2).mean(axis=1)  # (H,)
+    axes[0].plot(occ_mean, label=name, alpha=0.8)
+axes[0].set_xlabel("Step")
+axes[0].set_ylabel("Mean visible count")
+axes[0].set_title("Mean occupancy over time (across agents)")
+axes[0].legend()
+axes[0].grid(True, alpha=0.3)
+
+# Mean across timesteps (per agent)
+for name, (s, e, n_obj, n_feat) in zip(layer_names, layer_slices):
+    raw = buf_stoch["obs"][:, :, s:e].reshape(HORIZON, env.num_agents, n_obj, n_feat)
+    occ_per_agent = np.any(raw != 0, axis=3).sum(axis=2).mean(axis=0)  # (N,)
+    axes[1].bar(range(N), occ_per_agent, alpha=0.5, label=name)
+axes[1].set_xlabel("Agent")
+axes[1].set_ylabel("Mean visible count")
+axes[1].set_title("Mean occupancy per agent (across timesteps)")
+axes[1].legend()
+axes[1].grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+
+# Full obs sparsity: fraction of zero features per obs dimension, pooled
+all_flat = buf_stoch["obs"].reshape(-1, obs_dim)  # (H*N, obs_dim)
+zero_frac = (all_flat == 0).mean(axis=0)  # (obs_dim,)
+fig, ax = plt.subplots(figsize=(18, 3))
+ax.bar(range(obs_dim), zero_frac, width=1.0, color="steelblue", alpha=0.7)
+# Annotate layer boundaries
+prev_e = 0
+for name, (s, e) in slices.items():
+    ax.axvline(s, color="red", ls="--", lw=0.5, alpha=0.7)
+    mid = (s + e) / 2
+    ax.text(mid, 1.02, name, ha="center", va="bottom", fontsize=7, rotation=0, color="red")
+    prev_e = e
+ax.set_xlim(0, obs_dim)
+ax.set_ylim(0, 1.1)
+ax.set_xlabel("Obs dimension index")
+ax.set_ylabel("Fraction zero")
+ax.set_title("Per-dimension sparsity (fraction zero across full rollout)")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Policy outputs over time
+
+# %%
+# Compute action probs over time for tracked agent (stochastic rollout)
+n_actions = env.single_action_space.nvec[0] if not is_continuous else 1
+action_probs_time = np.zeros((HORIZON, n_actions))
+for t in range(HORIZON):
+    obs_t = torch.FloatTensor(buf_stoch["obs"][t : t + 1, TRACKED_AGENT : TRACKED_AGENT + 1][0]).to(device)
+    with torch.no_grad():
+        logits_list, _ = policy(obs_t)
+    logits = logits_list[0] if isinstance(logits_list, (list, tuple)) else logits_list
+    action_probs_time[t] = F.softmax(logits, dim=-1).cpu().numpy().flatten()
+
+fig, axes = plt.subplots(2, 2, figsize=(16, 10))
+
+# Action distribution heatmap
+im = axes[0, 0].imshow(action_probs_time.T, aspect="auto", cmap="viridis", interpolation="nearest")
+axes[0, 0].set_xlabel("Step")
+axes[0, 0].set_ylabel("Action ID")
+axes[0, 0].set_title(f"Action prob heatmap | agent={TRACKED_AGENT}")
+plt.colorbar(im, ax=axes[0, 0])
+
+# Entropy over time
+axes[0, 1].plot(buf_stoch["entropy"][:, TRACKED_AGENT], label="stochastic", alpha=0.8)
+axes[0, 1].set_xlabel("Step")
+axes[0, 1].set_ylabel("Entropy")
+axes[0, 1].set_title("Entropy over time")
+axes[0, 1].grid(True, alpha=0.3)
+
+# Value over time
+axes[1, 0].plot(buf_stoch["values"][:, TRACKED_AGENT], label="stochastic", alpha=0.8)
+axes[1, 0].plot(buf_det["values"][:, TRACKED_AGENT], label="deterministic", alpha=0.8)
+axes[1, 0].set_xlabel("Step")
+axes[1, 0].set_ylabel("Value")
+axes[1, 0].set_title("Value predictions over time")
+axes[1, 0].legend()
+axes[1, 0].grid(True, alpha=0.3)
+
+# Actions over time: deterministic vs stochastic
+axes[1, 1].step(range(HORIZON), buf_stoch["actions"][:, TRACKED_AGENT], label="stochastic", alpha=0.7)
+axes[1, 1].step(range(HORIZON), buf_det["actions"][:, TRACKED_AGENT], label="deterministic", alpha=0.7)
+axes[1, 1].set_xlabel("Step")
+axes[1, 1].set_ylabel("Action")
+axes[1, 1].set_title("Selected action over time")
+axes[1, 1].legend()
+axes[1, 1].grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Rewards and returns
+
+# %%
+fig, axes = plt.subplots(2, 3, figsize=(18, 10))
+
+# Per-step mean reward
+axes[0, 0].plot(buf_stoch["rewards"].mean(axis=1), label="stochastic", alpha=0.8)
+axes[0, 0].plot(buf_det["rewards"].mean(axis=1), label="deterministic", alpha=0.8)
+axes[0, 0].set_xlabel("Step")
+axes[0, 0].set_ylabel("Mean reward")
+axes[0, 0].set_title("Mean reward per step")
+axes[0, 0].legend()
+axes[0, 0].grid(True, alpha=0.3)
+
+# Reward heatmap (stochastic)
+im = axes[0, 1].imshow(buf_stoch["rewards"].T, aspect="auto", cmap="RdYlGn", interpolation="nearest")
+axes[0, 1].set_xlabel("Step")
+axes[0, 1].set_ylabel("Agent")
+axes[0, 1].set_title("Reward heatmap (stochastic)")
+plt.colorbar(im, ax=axes[0, 1])
+
+# Cumulative return per agent
+cum_ret_stoch = buf_stoch["rewards"].sum(axis=0)
+cum_ret_det = buf_det["rewards"].sum(axis=0)
+axes[0, 2].hist(cum_ret_stoch, bins=30, alpha=0.6, label="stochastic", edgecolor="black")
+axes[0, 2].hist(cum_ret_det, bins=30, alpha=0.6, label="deterministic", edgecolor="black")
+axes[0, 2].set_xlabel("Cumulative return")
+axes[0, 2].set_ylabel("Count")
+axes[0, 2].set_title("Return distribution across agents")
+axes[0, 2].legend()
+
+# Reward distribution histogram
+axes[1, 0].hist(buf_stoch["rewards"].flatten(), bins=50, alpha=0.7, edgecolor="black")
+axes[1, 0].set_xlabel("Reward")
+axes[1, 0].set_ylabel("Count")
+axes[1, 0].set_title("Per-step reward distribution (stochastic)")
+axes[1, 0].set_yscale("log")
+
+# Terminal/truncation timeline
+axes[1, 1].plot(buf_stoch["terminals"].sum(axis=1), label="terminals", alpha=0.8)
+axes[1, 1].plot(buf_stoch["truncations"].sum(axis=1), label="truncations", alpha=0.8)
+axes[1, 1].set_xlabel("Step")
+axes[1, 1].set_ylabel("Count")
+axes[1, 1].set_title("Terminals/Truncations per step (stochastic)")
+axes[1, 1].legend()
+axes[1, 1].grid(True, alpha=0.3)
+
+# Tracked agent reward
+axes[1, 2].plot(buf_stoch["rewards"][:, TRACKED_AGENT], label="stochastic", alpha=0.8)
+axes[1, 2].plot(buf_det["rewards"][:, TRACKED_AGENT], label="deterministic", alpha=0.8)
+axes[1, 2].set_xlabel("Step")
+axes[1, 2].set_ylabel("Reward")
+axes[1, 2].set_title(f"Reward over time | agent={TRACKED_AGENT}")
+axes[1, 2].legend()
+axes[1, 2].grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Episode metrics
+
+# %%
+# Collect episode-level metrics from the C logging
+log_stoch = binding.vec_log(env.c_envs, N)
+
+# eval_mode=1 returns a list of per-env dicts; aggregate by averaging
+if isinstance(log_stoch, list) and log_stoch:
+    all_keys = set(k for d in log_stoch for k in d if isinstance(d[k], (int, float)))
+    log_stoch = {k: np.mean([d[k] for d in log_stoch if k in d]) for k in all_keys}
+
+if log_stoch:
+    print("Episode metrics (after stochastic rollout):")
+    for k, v in sorted(log_stoch.items()):
+        if isinstance(v, (int, float)):
+            print(f"  {k}: {v:.4f}")
+
+    # Bar chart of key metrics
+    keys = ["score", "collision_rate", "offroad_rate", "completion_rate", "dnf_rate"]
+    vals = [log_stoch.get(k, 0) for k in keys]
+    fig, ax = plt.subplots(figsize=(10, 4))
+    bars = ax.bar(keys, vals, edgecolor="black", alpha=0.7, color=["green", "red", "orange", "blue", "gray"])
+    ax.set_ylabel("Rate")
+    ax.set_title("Episode Metrics")
+    for bar, v in zip(bars, vals):
+        ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.01, f"{v:.3f}", ha="center", fontsize=10)
+    plt.tight_layout()
+    plt.show()
+else:
+    print("No episode metrics available yet (not enough episodes completed)")
+
+# %% [markdown]
+# ## Value predictions vs actual returns
+
+# %%
+gamma = config["train"].get("gamma", 0.98)
+lam = config["train"].get("gae_lambda", 0.95)
+
+
+def compute_gae(rewards, values, terminals, truncations, gamma, lam):
+    H, N = rewards.shape
+    advantages = np.zeros_like(rewards)
+    last_gae = np.zeros(N)
+    for t in reversed(range(H - 1)):
+        done = np.maximum(terminals[t + 1], truncations[t + 1])
+        next_non_terminal = 1.0 - done
+        delta = rewards[t + 1] + gamma * values[t + 1] * next_non_terminal - values[t]
+        last_gae = delta + gamma * lam * last_gae * next_non_terminal
+        advantages[t] = last_gae
+    return advantages
+
+
+adv_stoch = compute_gae(
+    buf_stoch["rewards"], buf_stoch["values"], buf_stoch["terminals"], buf_stoch["truncations"], gamma, lam
+)
+returns_stoch = adv_stoch + buf_stoch["values"]
+
+pred_v = buf_stoch["values"].flatten()
+actual_r = returns_stoch.flatten()
+
+var_actual = np.var(actual_r)
+explained_var = 1 - np.var(actual_r - pred_v) / (var_actual + 1e-8) if var_actual > 1e-8 else 0.0
+
+fig, axes = plt.subplots(1, 3, figsize=(18, 5))
+
+# Scatter: predicted vs actual
+axes[0].scatter(actual_r, pred_v, alpha=0.2, s=5)
+lims = [min(actual_r.min(), pred_v.min()), max(actual_r.max(), pred_v.max())]
+axes[0].plot(lims, lims, "r--", label="perfect")
+axes[0].set_xlabel("Actual return")
+axes[0].set_ylabel("Predicted value")
+axes[0].set_title(f"Value accuracy (EV: {explained_var:.4f})")
+axes[0].legend()
+axes[0].grid(True, alpha=0.3)
+
+# Value error over time
+value_error = np.abs(returns_stoch - buf_stoch["values"]).mean(axis=1)
+axes[1].plot(value_error)
+axes[1].set_xlabel("Step")
+axes[1].set_ylabel("Mean |error|")
+axes[1].set_title("Value prediction error over time")
+axes[1].grid(True, alpha=0.3)
+
+# Advantage distribution
+axes[2].hist(adv_stoch.flatten(), bins=50, edgecolor="black", alpha=0.7)
+axes[2].set_xlabel("Advantage")
+axes[2].set_ylabel("Count")
+axes[2].set_title(f"Advantage distribution (std={adv_stoch.std():.4f})")
+
+plt.tight_layout()
+plt.show()
+
+print(f"Explained variance: {explained_var:.4f}")
+print(f"Value MSE: {np.mean((actual_r - pred_v) ** 2):.6f}")
+
+# %% [markdown]
+# ## Agent trajectories
+
+# %%
+N_TRAJ = min(16, N)  # number of agents to plot
+
+fig, axes = plt.subplots(1, 2, figsize=(16, 7))
+
+for i in range(N_TRAJ):
+    color = plt.cm.tab20(i % 20)
+    # Stochastic
+    axes[0].plot(buf_stoch["positions_x"][:, i], buf_stoch["positions_y"][:, i], alpha=0.6, color=color, linewidth=1)
+    axes[0].scatter(
+        buf_stoch["positions_x"][0, i], buf_stoch["positions_y"][0, i], color=color, s=30, marker="o", zorder=5
+    )  # start
+    # Deterministic
+    axes[1].plot(buf_det["positions_x"][:, i], buf_det["positions_y"][:, i], alpha=0.6, color=color, linewidth=1)
+    axes[1].scatter(buf_det["positions_x"][0, i], buf_det["positions_y"][0, i], color=color, s=30, marker="o", zorder=5)
+
+axes[0].set_title(f"Stochastic trajectories (N={N_TRAJ})")
+axes[1].set_title(f"Deterministic trajectories (N={N_TRAJ})")
+for ax in axes:
+    ax.set_xlabel("X")
+    ax.set_ylabel("Y")
+    ax.set_aspect("equal")
+    ax.grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+
+# ADE vs ground truth if scenario_length is set
+if config["env"].get("scenario_length"):
+    try:
+        gt = env.get_ground_truth_trajectories()
+        # gt['x'] shape: (N, 1, T), positions shape: (T, N)
+        gt_x = gt["x"][:, 0, :].T  # (T, N)
+        gt_y = gt["y"][:, 0, :].T
+        gt_valid = gt["valid"][:, 0, :].T
+        T_gt = gt_x.shape[0]
+        T_use = min(T_gt, HORIZON)
+
+        disp = np.sqrt(
+            (buf_stoch["positions_x"][:T_use] - gt_x[:T_use]) ** 2
+            + (buf_stoch["positions_y"][:T_use] - gt_y[:T_use]) ** 2
+        )
+        valid_mask = gt_valid[:T_use] > 0
+        if valid_mask.sum() > 0:
+            ade = disp[valid_mask].mean()
+            print(f"ADE (stochastic vs ground truth): {ade:.3f}m")
+            ade_per_agent = np.array(
+                [disp[:, i][valid_mask[:, i]].mean() for i in range(N) if valid_mask[:, i].sum() > 0]
+            )
+            plt.figure(figsize=(8, 3))
+            plt.hist(ade_per_agent, bins=30, edgecolor="black", alpha=0.7)
+            plt.xlabel("ADE (m)")
+            plt.ylabel("Count")
+            plt.title(f"Per-agent ADE distribution (mean={ade:.3f}m)")
+            plt.tight_layout()
+            plt.show()
+        else:
+            print("No valid ground truth timesteps to compute ADE")
+    except Exception as e:
+        print(f"Could not compute ADE: {e}")
+
+# %% [markdown]
+# ## Encoder analysis — what the policy encodes
+#
+# Each obs layer has its own encoder projecting raw features → embedding width:
+# - **ego** and **conditioning** (reward coefs + target): single vector, no pooling.
+# - **partners / lanes / boundaries / traffic**: per-slot encoder, padded slots masked to `-inf`, then **max-pooled** across slots → one embedding. Fully-padded layers are zeroed.
+#
+# The max-pool means each embedding dim is "won" by exactly one slot (object). Below we inspect:
+# 1. Encoder inventory (in/out dims, params).
+# 2. **What survives the max-pool**: which slot wins per dim, per-dim winner entropy (slot-specialized vs. spread), and where the dominant objects sit in ego frame.
+# 3. **Embedding space**: per-encoder contribution (L2 norm), active/dead dims, silence rate.
+
+# %%
+# ── Setup: capture per-encoder embeddings + reconstruct the max-pool ──
+bb = policy.actor_backbone
+ego_dim = policy.ego_dim
+PAD = -1.0  # PADDED_OBSERVATION_VALUE
+
+# Flat batch of observations from the stochastic rollout
+obs_flat = buf_stoch["obs"].reshape(-1, obs_dim)
+rng = np.random.default_rng(0)
+sel = rng.choice(obs_flat.shape[0], size=min(4096, obs_flat.shape[0]), replace=False)
+obs_batch = torch.FloatTensor(obs_flat[sel]).to(device)
+B = obs_batch.shape[0]
+
+# Encoder inventory: (name, module, raw_in_features, n_slots, is_set)
+enc_inventory = [("ego", bb.ego_encoder, ego_dim, 1, False)]
+if bb.obs_slots_lane_kept > 0:
+    enc_inventory.append(("lane", bb.lane_encoder, bb.road_features_count, bb.obs_slots_lane_kept, True))
+if bb.obs_slots_boundary_kept > 0:
+    enc_inventory.append(("boundary", bb.boundary_encoder, bb.road_features_count, bb.obs_slots_boundary_kept, True))
+if bb.obs_slots_partners_n > 0:
+    enc_inventory.append(("partner", bb.partner_encoder, bb.partner_features_count, bb.obs_slots_partners_n, True))
+if bb.obs_slots_traffic_controls_n > 0:
+    enc_inventory.append(
+        (
+            "traffic",
+            bb.traffic_control_encoder,
+            bb.traffic_control_features_after_onehot,
+            bb.obs_slots_traffic_controls_n,
+            True,
+        )
+    )
+if bb.target_dim > 0:
+    enc_inventory.append(("conditioning", bb.target_encoder, bb.target_dim, 1, False))
+
+enc_names = [n for n, *_ in enc_inventory]
+set_encs = [n for n, _, _, _, is_set in enc_inventory if is_set]
+
+print(f"{'encoder':>13s} | {'raw_in':>6s} | {'emb_out':>7s} | {'slots':>5s} | {'pooled':>6s} | {'params':>9s}")
+print("-" * 66)
+for name, mod, rin, nslots, is_set in enc_inventory:
+    nparam = sum(p.numel() for p in mod.parameters())
+    print(
+        f"{name:>13s} | {rin:>6d} | {mod[-1].out_features:>7d} | {nslots:>5d} | {('max' if is_set else '-'):>6s} | {nparam:>9,d}"
+    )
+print(
+    f"\nBackbone input = {sum(mod[-1].out_features for _, mod, _, _, _ in enc_inventory)} -> backbone -> {bb.out_dim}"
+)
+
+# Capture pre-pool encoder outputs via forward hooks
+captured = {}
+
+
+def _hook(name):
+    def fn(m, i, o):
+        captured[name] = o.detach()
+
+    return fn
+
+
+handles = [mod.register_forward_hook(_hook(name)) for name, mod, *_ in enc_inventory]
+policy.eval()
+with torch.no_grad():
+    policy(obs_batch)
+for h in handles:
+    h.remove()
+
+# Reconstruct slot slices (same order as DriveBackbone.forward) + pad masks
+partner_dim = bb.obs_slots_partners_n * bb.partner_features_count
+lane_dim = bb.obs_slots_lane_kept * bb.road_features_count
+boundary_dim = bb.obs_slots_boundary_kept * bb.road_features_count
+traffic_dim = bb.obs_slots_traffic_controls_n * bb.traffic_control_features_count
+_s = ego_dim + bb.target_dim
+sl = {}
+sl["partner"] = (_s, _s + partner_dim, bb.obs_slots_partners_n, bb.partner_features_count)
+_s += partner_dim
+sl["lane"] = (_s, _s + lane_dim, bb.obs_slots_lane_kept, bb.road_features_count)
+_s += lane_dim
+sl["boundary"] = (_s, _s + boundary_dim, bb.obs_slots_boundary_kept, bb.road_features_count)
+_s += boundary_dim
+sl["traffic"] = (_s, _s + traffic_dim, bb.obs_slots_traffic_controls_n, bb.traffic_control_features_count)
+_s += traffic_dim
+
+raw, pad, pooled, winners, valid_sample = {}, {}, {}, {}, {}
+for name in set_encs:
+    s, e, ns, nf = sl[name]
+    obj = obs_batch[:, s:e].view(B, ns, nf)
+    raw[name] = obj
+    if name == "traffic":
+        cont = obj[:, :, : bb.traffic_control_continuous_features]
+        typ = obj[:, :, bb.traffic_control_continuous_features]
+        st = obj[:, :, bb.traffic_control_continuous_features + 1]
+        pad[name] = (
+            (cont == PAD).all(dim=2)
+            & (typ == binding.TRAFFIC_CONTROL_TYPE_NONE)
+            & (st == binding.TRAFFIC_CONTROL_STATE_UNKNOWN)
+        )
+    else:
+        pad[name] = (obj == PAD).all(dim=2)
+    masked = captured[name].masked_fill(pad[name].unsqueeze(2), -torch.inf)
+    vm = (~pad[name]).any(dim=1)
+    valid_sample[name] = vm
+    winners[name] = masked.max(dim=1).indices  # (B, embedding dim): winning slot per dim
+    pooled[name] = torch.where(vm.unsqueeze(1), masked.max(dim=1).values, torch.zeros_like(masked.max(dim=1).values))
+
+for name in ("ego", "conditioning"):
+    if name in enc_names:
+        pooled[name] = captured[name]
+
+print("\nCaptured embeddings for:", enc_names)
+
+# %%
+# ── What survives the max-pool: winning slots, specialization, spatial ──
+n = len(set_encs)
+fig, axes = plt.subplots(n, 3, figsize=(18, 4.2 * n))
+if n == 1:
+    axes = axes[None, :]
+
+print(f"{'encoder':>9s} | {'valid%':>6s} | {'mean active slots/dim':>21s} | {'%slot-specialized dims':>22s}")
+print("-" * 70)
+for r, name in enumerate(set_encs):
+    s, e, ns, nf = sl[name]
+    vm = valid_sample[name]
+    w = winners[name][vm]  # (Bv, D)
+    D = w.shape[1]
+
+    # (1) which slot wins, pooled over all dims+samples
+    slot_counts = torch.bincount(w.reshape(-1), minlength=ns).float().cpu().numpy()
+    slot_counts = slot_counts / max(slot_counts.sum(), 1)
+    axes[r, 0].bar(range(ns), slot_counts, color="teal", alpha=0.85, edgecolor="black")
+    axes[r, 0].set_title(f"{name}: max-pool winner by slot")
+    axes[r, 0].set_xlabel("slot index (0 = first/closest)")
+    axes[r, 0].set_ylabel("frac of dims won")
+
+    # (2) per-dim winner entropy: slot-specialized (0) vs spread across slots (1)
+    onehot = F.one_hot(w, num_classes=ns).float()  # (Bv, D, ns)
+    p = onehot.mean(dim=0)  # (D, ns) winner distribution per dim
+    ent = (-(p * (p + 1e-9).log()).sum(dim=1) / np.log(ns)).cpu().numpy()
+    axes[r, 1].hist(ent, bins=30, color="indianred", alpha=0.85, edgecolor="black")
+    axes[r, 1].set_title(f"{name}: per-dim winner entropy")
+    axes[r, 1].set_xlabel("0 = slot-specialized   →   1 = spread")
+    axes[r, 1].set_xlim(0, 1)
+
+    # (3) ego-frame position of the dominant object (mode winning slot per sample)
+    dom = torch.mode(w, dim=1).values  # (Bv,)
+    rel = raw[name][vm]
+    dom_xy = rel[torch.arange(rel.shape[0]), dom][:, :2].cpu().numpy()
+    axes[r, 2].scatter(dom_xy[:, 0], dom_xy[:, 1], s=3, alpha=0.15, color="navy")
+    axes[r, 2].scatter(0, 0, marker="*", s=200, color="red", zorder=5, label="ego")
+    axes[r, 2].set_title(f"{name}: dominant object position (ego frame)")
+    axes[r, 2].set_xlabel("rel_x")
+    axes[r, 2].set_ylabel("rel_y")
+    axes[r, 2].set_aspect("equal")
+    axes[r, 2].legend(fontsize=8)
+
+    active_per_dim = np.exp(ent * np.log(ns)).mean()
+    print(
+        f"{name:>9s} | {100 * vm.float().mean().item():>5.1f}% | {active_per_dim:>21.2f} | {100 * (ent < 0.2).mean():>21.1f}%"
+    )
+
+plt.tight_layout()
+plt.show()
+
+
+# ── H1/H2/H3 check: boundary max-pool winner distance vs slot index ──
+if "boundary" in set_encs:
+    vm = valid_sample["boundary"]
+    w = winners["boundary"][vm]  # (Bv, D) winning slot per dim
+    rb = raw["boundary"][vm]  # (Bv, ns, nf) raw segments
+    nsb = rb.shape[1]
+    reldist = torch.hypot(rb[:, :, 0], rb[:, :, 1])  # (Bv, ns) normalized ego-frame dist
+    valid_seg = ~pad["boundary"][vm]  # (Bv, ns) slots holding a real segment
+
+    win_reldist = torch.gather(reldist, 1, w)  # (Bv, D) dist of each winning segment
+    slot_flat = w.reshape(-1)
+    wdist_flat = win_reldist.reshape(-1)
+    total_wins = slot_flat.numel()
+
+    print("\n=== Boundary winner distance vs slot index (H1/H2/H3) ===")
+    print(f"{'slot':>4s} | {'#wins':>8s} | {'win%':>6s} | {'rel_dist winners':>16s} | {'rel_dist occupied':>17s}")
+    print("-" * 64)
+    for s in range(nsb):
+        wm = slot_flat == s
+        nwin = int(wm.sum())
+        wmean = wdist_flat[wm].mean().item() if nwin > 0 else float("nan")
+        occ = valid_seg[:, s]
+        omean = reldist[occ, s].mean().item() if int(occ.sum()) > 0 else float("nan")
+        print(f"{s:>4d} | {nwin:>8d} | {100 * nwin / total_wins:>5.1f}% | {wmean:>16.4f} | {omean:>17.4f}")
+
+    win_mean = wdist_flat.mean().item()
+    seg_mean = reldist[valid_seg].mean().item()
+    print(f"\nMean rel_dist of WINNING segments : {win_mean:.4f}")
+    print(f"Mean rel_dist of ALL valid segments: {seg_mean:.4f}")
+    verdict = "FARTHER than avg (H3 supported)" if win_mean > seg_mean else "nearer than avg"
+    print(f"-> winners are {verdict} by {win_mean - seg_mean:+.4f} (normalized units)")
+
+    fig, ax = plt.subplots(1, 2, figsize=(14, 4))
+    occ_means = [
+        reldist[valid_seg[:, s], s].mean().item() if int(valid_seg[:, s].sum()) > 0 else np.nan for s in range(nsb)
+    ]
+    win_means = [
+        wdist_flat[slot_flat == s].mean().item() if int((slot_flat == s).sum()) > 0 else np.nan for s in range(nsb)
+    ]
+    ax[0].plot(range(nsb), occ_means, "o-", label="occupied (any segment in slot)")
+    ax[0].plot(range(nsb), win_means, "s-", label="winners only")
+    ax[0].axhline(seg_mean, color="gray", ls="--", label="global valid mean")
+    ax[0].set_xlabel("slot index")
+    ax[0].set_ylabel("mean rel_dist (normalized)")
+    ax[0].set_title("Boundary rel_dist vs slot index\n(flat = not distance-sorted -> H1/H2)")
+    ax[0].legend(fontsize=8)
+    ax[1].hist(reldist[valid_seg].cpu().numpy(), bins=50, alpha=0.6, density=True, label="all valid segs", color="gray")
+    ax[1].hist(wdist_flat.cpu().numpy(), bins=50, alpha=0.6, density=True, label="winners", color="crimson")
+    ax[1].axvline(seg_mean, color="gray", ls="--")
+    ax[1].axvline(win_mean, color="crimson", ls="--")
+    ax[1].set_xlabel("rel_dist (normalized)")
+    ax[1].set_title("Winner vs all-segment distance (H3)")
+    ax[1].legend(fontsize=8)
+    plt.tight_layout()
+    plt.show()
+
+# %%
+# ── Embedding space: per-encoder contribution, active/dead dims, silence ──
+fig, axes = plt.subplots(1, 3, figsize=(20, 5))
+
+# (1) Mean L2 norm of each pooled embedding = relative weight in the concat fed to backbone
+norms = [pooled[n].norm(dim=1).mean().item() for n in enc_names]
+axes[0].bar(enc_names, norms, color="slateblue", edgecolor="black")
+axes[0].set_title("Mean L2 norm of pooled embedding\n(relative contribution to backbone input)")
+axes[0].tick_params(axis="x", rotation=45)
+axes[0].grid(True, axis="y", alpha=0.3)
+
+# (2) Mean |activation| per embedding dim, per encoder
+M = np.stack([pooled[n].abs().mean(0).cpu().numpy() for n in enc_names])
+im = axes[1].imshow(M, aspect="auto", cmap="magma")
+axes[1].set_yticks(range(len(enc_names)))
+axes[1].set_yticklabels(enc_names)
+axes[1].set_xlabel("embedding dim")
+axes[1].set_title("Mean |activation| per embedding dim")
+plt.colorbar(im, ax=axes[1])
+
+# (3) Dead dims (std<1e-4) — capacity the encoder never uses
+dead = [(pooled[n].std(0) < 1e-4).float().mean().item() for n in enc_names]
+axes[2].bar(enc_names, dead, color="gray", edgecolor="black")
+axes[2].set_title("Fraction of dead embedding dims (std < 1e-4)")
+axes[2].tick_params(axis="x", rotation=45)
+axes[2].set_ylim(0, 1)
+axes[2].grid(True, axis="y", alpha=0.3)
+
+plt.tight_layout()
+plt.show()
+
+print(f"{'encoder':>13s} | {'mean|act|':>9s} | {'emb L2':>7s} | {'dead dims':>9s} | {'silence (fully padded)':>22s}")
+print("-" * 80)
+for name in enc_names:
+    silence = (1 - valid_sample[name].float().mean().item()) if name in valid_sample else 0.0
+    deadf = (pooled[name].std(0) < 1e-4).float().mean().item()
+    print(
+        f"{name:>13s} | {pooled[name].abs().mean().item():>9.4f} | {pooled[name].norm(dim=1).mean().item():>7.3f} | "
+        f"{100 * deadf:>7.1f}% | {100 * silence:>21.1f}%"
+    )
diff --git a/notebooks/06_architecture.ipynb b/notebooks/06_architecture.ipynb
deleted file mode 100644
index 7a799a2abf..0000000000
--- a/notebooks/06_architecture.ipynb
+++ /dev/null
@@ -1,813 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 06 - Neural Network Architecture\n",
-    "Visualize, analyze, and iterate on the DrivePolicy architecture. Covers model summary, per-encoder breakdown, forward pass shape tracing, weight distributions, and architecture comparison."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from torchinfo import summary\n",
-    "from pufferlib.ocean.drive import binding\n",
-    "from pufferlib.ocean.torch import Drive as DrivePolicy\n",
-    "from notebooks.notebook_utils import make_drive_env, zero_actions\n",
-    "\n",
-    "# --- Policy architecture ---\n",
-    "INPUT_SIZE = 64\n",
-    "BACKBONE_HIDDEN_SIZE = 1024\n",
-    "BACKBONE_NUM_LAYERS = 3\n",
-    "ACTOR_HIDDEN_SIZE = 128\n",
-    "ACTOR_NUM_LAYERS = 3\n",
-    "CRITIC_HIDDEN_SIZE = 64\n",
-    "CRITIC_NUM_LAYERS = 2\n",
-    "SHARED_NETWORK = True\n",
-    "ENCODER_ACTIVATION = \"tanh\"\n",
-    "ENCODER_LAYER_NORM = True\n",
-    "BACKBONE_ACTIVATION = \"gelu\"\n",
-    "BACKBONE_LAYER_NORM = False\n",
-    "\n",
-    "env, obs, info = make_drive_env()\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "policy = DrivePolicy(\n",
-    "    env,\n",
-    "    ego_input_size=INPUT_SIZE,\n",
-    "    partner_input_size=INPUT_SIZE,\n",
-    "    lane_input_size=INPUT_SIZE,\n",
-    "    boundary_input_size=INPUT_SIZE,\n",
-    "    traffic_control_input_size=INPUT_SIZE,\n",
-    "    target_input_size=INPUT_SIZE,\n",
-    "    backbone_hidden_size=BACKBONE_HIDDEN_SIZE,\n",
-    "    backbone_num_layers=BACKBONE_NUM_LAYERS,\n",
-    "    actor_hidden_size=ACTOR_HIDDEN_SIZE,\n",
-    "    actor_num_layers=ACTOR_NUM_LAYERS,\n",
-    "    critic_hidden_size=CRITIC_HIDDEN_SIZE,\n",
-    "    critic_num_layers=CRITIC_NUM_LAYERS,\n",
-    "    encoder_activation=ENCODER_ACTIVATION,\n",
-    "    encoder_layer_norm=ENCODER_LAYER_NORM,\n",
-    "    backbone_activation=BACKBONE_ACTIVATION,\n",
-    "    backbone_layer_norm=BACKBONE_LAYER_NORM,\n",
-    "    shared_network=SHARED_NETWORK,\n",
-    ").to(device)\n",
-    "\n",
-    "print(f\"Device: {device}\")\n",
-    "print(f\"Obs dim: {obs.shape[1]}\")\n",
-    "print(f\"Action dim: {policy.atn_dim}\")\n",
-    "print(f\"Shared network: {SHARED_NETWORK}\")\n",
-    "print(f\"Backbone: {BACKBONE_HIDDEN_SIZE} x {BACKBONE_NUM_LAYERS}L\")\n",
-    "print(f\"Actor: {ACTOR_HIDDEN_SIZE} x {ACTOR_NUM_LAYERS}L\")\n",
-    "print(f\"Critic: {CRITIC_HIDDEN_SIZE} x {CRITIC_NUM_LAYERS}L\")\n",
-    "print(f\"Encoder: {ENCODER_ACTIVATION}, LayerNorm: {ENCODER_LAYER_NORM}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Model Summary (torchinfo)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "obs_tensor = torch.FloatTensor(obs).to(device)\n",
-    "summary(policy, input_data=obs_tensor, depth=4, col_names=[\"input_size\", \"output_size\", \"num_params\", \"mult_adds\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Architecture Diagram"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "backbone = policy.actor_backbone\n",
-    "cond_dim = backbone.target_dim\n",
-    "\n",
-    "# Collect encoder info\n",
-    "encoders = [\n",
-    "    (\"ego\", env.ego_features, 1, \"direct\", INPUT_SIZE),\n",
-    "    (\"conditioning\", cond_dim, 1, \"direct\", INPUT_SIZE) if cond_dim > 0 else None,\n",
-    "    (\"partner\", env.partner_features, env.obs_slots_partners_n, \"max-pool\", INPUT_SIZE),\n",
-    "    (\"lane\", env.road_features, env.obs_slots_lane_kept, \"max-pool\", INPUT_SIZE),\n",
-    "    (\"boundary\", env.road_features, env.obs_slots_boundary_kept, \"max-pool\", INPUT_SIZE),\n",
-    "    (\n",
-    "        \"traffic_ctrl\",\n",
-    "        env.traffic_control_features - 2 + binding.NUM_TRAFFIC_CONTROL_TYPES + binding.NUM_TRAFFIC_CONTROL_STATES,\n",
-    "        env.obs_slots_traffic_controls_n,\n",
-    "        \"max-pool (onehot)\",\n",
-    "        INPUT_SIZE,\n",
-    "    ),\n",
-    "]\n",
-    "encoders = [e for e in encoders if e is not None]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(14, 8))\n",
-    "ax.set_xlim(0, 10)\n",
-    "ax.set_ylim(0, 10)\n",
-    "ax.axis(\"off\")\n",
-    "\n",
-    "n_enc = len(encoders)\n",
-    "y_positions = np.linspace(9, 1, n_enc)\n",
-    "colors = plt.cm.Set2(np.linspace(0, 1, n_enc))\n",
-    "\n",
-    "# Draw encoders\n",
-    "for i, ((name, in_f, n_obj, agg, out_size), y, c) in enumerate(zip(encoders, y_positions, colors)):\n",
-    "    # Input box\n",
-    "    label = f\"{name}\\n{n_obj}x{in_f}\" if n_obj > 1 else f\"{name}\\n{in_f}\"\n",
-    "    ax.add_patch(plt.Rectangle((0.2, y - 0.3), 1.6, 0.6, facecolor=c, edgecolor=\"black\", lw=1.2, alpha=0.8))\n",
-    "    ax.text(1.0, y, label, ha=\"center\", va=\"center\", fontsize=8, fontweight=\"bold\")\n",
-    "\n",
-    "    # Encoder box\n",
-    "    ax.add_patch(plt.Rectangle((2.5, y - 0.25), 2.0, 0.5, facecolor=\"lightyellow\", edgecolor=\"black\", lw=1))\n",
-    "    ax.text(3.5, y + 0.05, f\"Linear({in_f},{out_size})\", ha=\"center\", va=\"center\", fontsize=7)\n",
-    "    ln_label = \"LN+\" if ENCODER_LAYER_NORM else \"\"\n",
-    "    ax.text(\n",
-    "        3.5,\n",
-    "        y - 0.12,\n",
-    "        f\"{ln_label}{ENCODER_ACTIVATION}+Linear({out_size},{out_size})\",\n",
-    "        ha=\"center\",\n",
-    "        va=\"center\",\n",
-    "        fontsize=6,\n",
-    "        color=\"gray\",\n",
-    "    )\n",
-    "\n",
-    "    # Aggregation\n",
-    "    if n_obj > 1:\n",
-    "        ax.text(5.0, y, agg, ha=\"center\", va=\"center\", fontsize=7, style=\"italic\", color=\"darkblue\")\n",
-    "        arrow_start = 5.5\n",
-    "    else:\n",
-    "        arrow_start = 4.6\n",
-    "\n",
-    "    # Arrows\n",
-    "    ax.annotate(\"\", xy=(2.5, y), xytext=(1.8, y), arrowprops=dict(arrowstyle=\"->\", lw=1))\n",
-    "    ax.annotate(\"\", xy=(6.0, 5.0), xytext=(arrow_start, y), arrowprops=dict(arrowstyle=\"->\", lw=0.8, color=\"gray\"))\n",
-    "\n",
-    "# Concat box\n",
-    "ax.add_patch(plt.Rectangle((5.8, 4.5), 1.4, 1.0, facecolor=\"lightsalmon\", edgecolor=\"black\", lw=1.5))\n",
-    "ax.text(6.5, 5.2, \"Concat\", ha=\"center\", va=\"center\", fontsize=9, fontweight=\"bold\")\n",
-    "ax.text(6.5, 4.85, f\"{n_enc}x{INPUT_SIZE}={n_enc * INPUT_SIZE}\", ha=\"center\", va=\"center\", fontsize=7)\n",
-    "\n",
-    "# Backbone\n",
-    "ax.add_patch(plt.Rectangle((7.5, 4.5), 1.3, 1.0, facecolor=\"lightblue\", edgecolor=\"black\", lw=1.5))\n",
-    "ax.text(8.15, 5.15, f\"Backbone ({BACKBONE_NUM_LAYERS}L)\", ha=\"center\", va=\"center\", fontsize=8, fontweight=\"bold\")\n",
-    "ax.text(8.15, 4.85, f\"GELU+Linear\\n({n_enc * INPUT_SIZE},{BACKBONE_HIDDEN_SIZE})\", ha=\"center\", va=\"center\", fontsize=6)\n",
-    "ax.annotate(\"\", xy=(7.5, 5.0), xytext=(7.2, 5.0), arrowprops=dict(arrowstyle=\"->\", lw=1.5))\n",
-    "\n",
-    "# Actor / Critic heads\n",
-    "ax.add_patch(plt.Rectangle((9.0, 5.7), 0.9, 0.6, facecolor=\"lightgreen\", edgecolor=\"black\", lw=1.2))\n",
-    "actor_label = f\"Actor ({ACTOR_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->{sum(policy.atn_dim)}\"\n",
-    "if ACTOR_NUM_LAYERS > 1:\n",
-    "    actor_label = (\n",
-    "        f\"Actor ({ACTOR_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->{ACTOR_HIDDEN_SIZE}->...->{sum(policy.atn_dim)}\"\n",
-    "    )\n",
-    "ax.text(9.45, 6.0, actor_label, ha=\"center\", va=\"center\", fontsize=6, fontweight=\"bold\")\n",
-    "\n",
-    "ax.add_patch(plt.Rectangle((9.0, 3.7), 0.9, 0.6, facecolor=\"plum\", edgecolor=\"black\", lw=1.2))\n",
-    "critic_label = f\"Critic ({CRITIC_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->1\"\n",
-    "if CRITIC_NUM_LAYERS > 1:\n",
-    "    critic_label = f\"Critic ({CRITIC_NUM_LAYERS}L)\\n{BACKBONE_HIDDEN_SIZE}->{CRITIC_HIDDEN_SIZE}->...->1\"\n",
-    "ax.text(9.45, 4.0, critic_label, ha=\"center\", va=\"center\", fontsize=6, fontweight=\"bold\")\n",
-    "\n",
-    "ax.annotate(\"\", xy=(9.0, 6.0), xytext=(8.8, 5.3), arrowprops=dict(arrowstyle=\"->\", lw=1.2))\n",
-    "ax.annotate(\"\", xy=(9.0, 4.0), xytext=(8.8, 4.7), arrowprops=dict(arrowstyle=\"->\", lw=1.2))\n",
-    "\n",
-    "split_label = \"SHARED\" if SHARED_NETWORK else \"SPLIT\"\n",
-    "ax.text(8.9, 4.55, split_label, ha=\"center\", va=\"center\", fontsize=7, color=\"red\", fontweight=\"bold\")\n",
-    "\n",
-    "ax.text(\n",
-    "    5.0,\n",
-    "    0.3,\n",
-    "    f\"Encoder: {ENCODER_ACTIVATION} | LayerNorm: {ENCODER_LAYER_NORM}\",\n",
-    "    ha=\"center\",\n",
-    "    va=\"center\",\n",
-    "    fontsize=8,\n",
-    "    color=\"darkgreen\",\n",
-    "    fontweight=\"bold\",\n",
-    ")\n",
-    "\n",
-    "ax.set_title(\n",
-    "    f\"DrivePolicy Architecture (encoder_size={INPUT_SIZE}, backbone={BACKBONE_HIDDEN_SIZE})\",\n",
-    "    fontsize=12,\n",
-    "    fontweight=\"bold\",\n",
-    ")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Per-Encoder Parameter Breakdown"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_params(module):\n",
-    "    return sum(p.numel() for p in module.parameters())\n",
-    "\n",
-    "\n",
-    "backbone = policy.actor_backbone\n",
-    "components = {\n",
-    "    \"ego_encoder\": backbone.ego_encoder,\n",
-    "    \"lane_encoder\": backbone.lane_encoder,\n",
-    "    \"boundary_encoder\": backbone.boundary_encoder,\n",
-    "    \"partner_encoder\": backbone.partner_encoder,\n",
-    "    \"traffic_ctrl_encoder\": backbone.traffic_control_encoder,\n",
-    "}\n",
-    "if backbone.target_dim > 0:\n",
-    "    components[\"target_encoder\"] = backbone.target_encoder\n",
-    "components[\"backbone_mlp\"] = backbone.backbone\n",
-    "components[\"actor_head\"] = policy.actor_head\n",
-    "components[\"critic_head\"] = policy.critic_head\n",
-    "\n",
-    "names, counts = zip(*[(k, count_params(v)) for k, v in components.items()])\n",
-    "total = sum(counts)\n",
-    "\n",
-    "print(f\"{'Component':>25s} | {'Params':>10s} | {'%':>6s}\")\n",
-    "print(\"-\" * 48)\n",
-    "for n, c in zip(names, counts):\n",
-    "    print(f\"{n:>25s} | {c:>10,d} | {c / total:>5.1%}\")\n",
-    "print(\"-\" * 48)\n",
-    "print(f\"{'TOTAL':>25s} | {total:>10,d}\")\n",
-    "if not SHARED_NETWORK:\n",
-    "    critic_bb = count_params(policy.critic_backbone)\n",
-    "    print(f\"{'+ critic_backbone':>25s} | {critic_bb:>10,d}\")\n",
-    "    print(f\"{'GRAND TOTAL':>25s} | {total + critic_bb:>10,d}\")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 5))\n",
-    "colors = plt.cm.Set3(np.linspace(0, 1, len(names)))\n",
-    "bars = ax.barh(names, counts, color=colors, edgecolor=\"black\")\n",
-    "for bar, c in zip(bars, counts):\n",
-    "    ax.text(bar.get_width() + total * 0.01, bar.get_y() + bar.get_height() / 2, f\"{c:,}\", va=\"center\", fontsize=8)\n",
-    "ax.set_xlabel(\"Parameters\")\n",
-    "ax.set_title(f\"Parameter Distribution ({total:,} total)\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Forward Pass Shape Trace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = obs_tensor\n",
-    "backbone = policy.actor_backbone\n",
-    "\n",
-    "slide_idx = env.ego_features\n",
-    "cond_dim = backbone.target_dim\n",
-    "partner_dim = env.obs_slots_partners_n * env.partner_features\n",
-    "lane_dim = env.obs_slots_lane_kept * env.road_features\n",
-    "boundary_dim = env.obs_slots_boundary_kept * env.road_features\n",
-    "traffic_dim = env.obs_slots_traffic_controls_n * env.traffic_control_features\n",
-    "\n",
-    "# Slicing\n",
-    "ego_obs = x[:, :slide_idx]\n",
-    "slices = [(\"ego\", 0, slide_idx, ego_obs.shape)]\n",
-    "\n",
-    "if cond_dim > 0:\n",
-    "    cond_obs = x[:, slide_idx : slide_idx + cond_dim]\n",
-    "    slices.append((\"conditioning\", slide_idx, slide_idx + cond_dim, cond_obs.shape))\n",
-    "    slide_idx += cond_dim\n",
-    "\n",
-    "partner_obs = x[:, slide_idx : slide_idx + partner_dim]\n",
-    "slices.append((\"partners\", slide_idx, slide_idx + partner_dim, partner_obs.shape))\n",
-    "slide_idx += partner_dim\n",
-    "\n",
-    "lane_obs = x[:, slide_idx : slide_idx + lane_dim]\n",
-    "slices.append((\"lanes\", slide_idx, slide_idx + lane_dim, lane_obs.shape))\n",
-    "slide_idx += lane_dim\n",
-    "\n",
-    "boundary_obs = x[:, slide_idx : slide_idx + boundary_dim]\n",
-    "slices.append((\"boundaries\", slide_idx, slide_idx + boundary_dim, boundary_obs.shape))\n",
-    "slide_idx += boundary_dim\n",
-    "\n",
-    "traffic_obs = x[:, slide_idx : slide_idx + traffic_dim]\n",
-    "slices.append((\"traffic_ctrl\", slide_idx, slide_idx + traffic_dim, traffic_obs.shape))\n",
-    "\n",
-    "print(f\"Obs buffer layout (total={x.shape[1]}):\")\n",
-    "print(f\"{'Name':>15s} | {'Start':>5s} | {'End':>5s} | {'Width':>5s} | Shape\")\n",
-    "print(\"-\" * 65)\n",
-    "for name, start, end, shape in slices:\n",
-    "    print(f\"{name:>15s} | {start:>5d} | {end:>5d} | {end - start:>5d} | {shape}\")\n",
-    "\n",
-    "# Forward through encoders\n",
-    "print(\"\\nEncoder outputs:\")\n",
-    "with torch.no_grad():\n",
-    "    ego_enc = backbone.ego_encoder(ego_obs)\n",
-    "    print(f\"  ego_encoder:     {ego_obs.shape} -> {ego_enc.shape}\")\n",
-    "\n",
-    "    if cond_dim > 0:\n",
-    "        cond_enc = backbone.target_encoder(cond_obs)\n",
-    "        print(f\"  cond_encoder:    {cond_obs.shape} -> {cond_enc.shape}\")\n",
-    "\n",
-    "    p_reshaped = partner_obs.view(-1, env.obs_slots_partners_n, env.partner_features)\n",
-    "    p_enc, _ = backbone.partner_encoder(p_reshaped).max(dim=1)\n",
-    "    print(f\"  partner_encoder: {partner_obs.shape} -> view {p_reshaped.shape} -> encode -> max-pool -> {p_enc.shape}\")\n",
-    "\n",
-    "    l_reshaped = lane_obs.view(-1, env.obs_slots_lane_kept, env.road_features)\n",
-    "    l_enc, _ = backbone.lane_encoder(l_reshaped).max(dim=1)\n",
-    "    print(f\"  lane_encoder:    {lane_obs.shape} -> view {l_reshaped.shape} -> encode -> max-pool -> {l_enc.shape}\")\n",
-    "\n",
-    "    b_reshaped = boundary_obs.view(-1, env.obs_slots_boundary_kept, env.road_features)\n",
-    "    b_enc, _ = backbone.boundary_encoder(b_reshaped).max(dim=1)\n",
-    "    print(f\"  bound_encoder:   {boundary_obs.shape} -> view {b_reshaped.shape} -> encode -> max-pool -> {b_enc.shape}\")\n",
-    "\n",
-    "    t_reshaped = traffic_obs.view(-1, env.obs_slots_traffic_controls_n, env.traffic_control_features)\n",
-    "    t_cont = t_reshaped[:, :, : env.traffic_control_features - 2]\n",
-    "    t_type = t_reshaped[:, :, env.traffic_control_features - 2]\n",
-    "    t_state = t_reshaped[:, :, env.traffic_control_features - 1]\n",
-    "    t_type_onehot = F.one_hot(t_type.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_TYPES).float()\n",
-    "    t_state_onehot = F.one_hot(t_state.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_STATES).float()\n",
-    "    t_input = torch.cat([t_cont, t_type_onehot, t_state_onehot], dim=2)\n",
-    "    t_enc, _ = backbone.traffic_control_encoder(t_input).max(dim=1)\n",
-    "    print(\n",
-    "        f\"  traffic_encoder: {traffic_obs.shape} -> view {t_reshaped.shape} -> onehot {t_input.shape} -> encode -> max-pool -> {t_enc.shape}\"\n",
-    "    )\n",
-    "\n",
-    "    # Concat + backbone\n",
-    "    features = [ego_enc, l_enc, b_enc, p_enc, t_enc]\n",
-    "    if cond_dim > 0:\n",
-    "        features.append(cond_enc)\n",
-    "    concat = torch.cat(features, dim=1)\n",
-    "    hidden = backbone.backbone(concat)\n",
-    "    print(f\"\\n  concat: {concat.shape}\")\n",
-    "    print(f\"  backbone_mlp: {concat.shape} -> {hidden.shape}\")\n",
-    "\n",
-    "    # Heads\n",
-    "    actor_out = policy.actor_head(hidden)\n",
-    "    critic_out = policy.critic_head(hidden)\n",
-    "    print(f\"  actor_head:  {hidden.shape} -> {actor_out.shape} (split into {policy.atn_dim})\")\n",
-    "    print(f\"  critic_head: {hidden.shape} -> {critic_out.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Weight Distributions by Layer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weight_data = [\n",
-    "    (n, p.data.cpu().numpy().flatten()) for n, p in policy.named_parameters() if \"weight\" in n and p.dim() >= 2\n",
-    "]\n",
-    "\n",
-    "n_weights = len(weight_data)\n",
-    "cols = 4\n",
-    "rows = (n_weights + cols - 1) // cols\n",
-    "fig, axes = plt.subplots(rows, cols, figsize=(4 * cols, 3 * rows))\n",
-    "axes = axes.flatten()\n",
-    "\n",
-    "for i, (name, w) in enumerate(weight_data):\n",
-    "    ax = axes[i]\n",
-    "    ax.hist(w, bins=50, edgecolor=\"black\", alpha=0.7, density=True)\n",
-    "    ax.set_title(name.replace(\"actor_backbone.\", \"\"), fontsize=7)\n",
-    "    ax.axvline(0, color=\"red\", ls=\"--\", lw=0.5)\n",
-    "    ax.text(0.95, 0.95, f\"std={w.std():.3f}\", transform=ax.transAxes, fontsize=6, ha=\"right\", va=\"top\")\n",
-    "\n",
-    "for j in range(i + 1, len(axes)):\n",
-    "    axes[j].axis(\"off\")\n",
-    "\n",
-    "fig.suptitle(\"Weight Distributions (init)\", fontsize=12, fontweight=\"bold\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Activation Analysis (per encoder)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "policy.eval()\n",
-    "with torch.no_grad():\n",
-    "    hidden = policy.actor_backbone(obs_tensor, env.ego_features)\n",
-    "    action_logits, value = policy.decode_actions(hidden)\n",
-    "\n",
-    "# Collect per-encoder activations\n",
-    "activations = {}\n",
-    "with torch.no_grad():\n",
-    "    slide = env.ego_features\n",
-    "    activations[\"ego\"] = backbone.ego_encoder(obs_tensor[:, : env.ego_features])\n",
-    "\n",
-    "    if cond_dim > 0:\n",
-    "        activations[\"conditioning\"] = backbone.target_encoder(obs_tensor[:, slide : slide + cond_dim])\n",
-    "        slide += cond_dim\n",
-    "\n",
-    "    p_obs = obs_tensor[:, slide : slide + partner_dim].view(-1, env.obs_slots_partners_n, env.partner_features)\n",
-    "    activations[\"partner\"], _ = backbone.partner_encoder(p_obs).max(dim=1)\n",
-    "    slide += partner_dim\n",
-    "\n",
-    "    l_obs = obs_tensor[:, slide : slide + lane_dim].view(-1, env.obs_slots_lane_kept, env.road_features)\n",
-    "    activations[\"lane\"], _ = backbone.lane_encoder(l_obs).max(dim=1)\n",
-    "    slide += lane_dim\n",
-    "\n",
-    "    b_obs = obs_tensor[:, slide : slide + boundary_dim].view(-1, env.obs_slots_boundary_kept, env.road_features)\n",
-    "    activations[\"boundary\"], _ = backbone.boundary_encoder(b_obs).max(dim=1)\n",
-    "    slide += boundary_dim\n",
-    "\n",
-    "    t_obs = obs_tensor[:, slide : slide + traffic_dim].view(\n",
-    "        -1, env.obs_slots_traffic_controls_n, env.traffic_control_features\n",
-    "    )\n",
-    "    t_cont = t_obs[:, :, : env.traffic_control_features - 2]\n",
-    "    t_type = t_obs[:, :, env.traffic_control_features - 2]\n",
-    "    t_state = t_obs[:, :, env.traffic_control_features - 1]\n",
-    "    t_type_onehot = F.one_hot(t_type.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_TYPES).float()\n",
-    "    t_state_onehot = F.one_hot(t_state.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_STATES).float()\n",
-    "    t_input = torch.cat([t_cont, t_type_onehot, t_state_onehot], dim=2)\n",
-    "    activations[\"traffic_ctrl\"], _ = backbone.traffic_control_encoder(t_input).max(dim=1)\n",
-    "\n",
-    "    activations[\"hidden\"] = hidden\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 4, figsize=(16, 6))\n",
-    "axes = axes.flatten()\n",
-    "for i, (name, act) in enumerate(activations.items()):\n",
-    "    if i >= len(axes):\n",
-    "        break\n",
-    "    vals = act.cpu().numpy().flatten()\n",
-    "    ax = axes[i]\n",
-    "    ax.hist(vals, bins=50, edgecolor=\"black\", alpha=0.7)\n",
-    "    dead = (act.abs().sum(dim=0) == 0).sum().item()\n",
-    "    ax.set_title(f\"{name} (dead={dead}/{act.shape[1]})\", fontsize=9)\n",
-    "    ax.text(\n",
-    "        0.95,\n",
-    "        0.95,\n",
-    "        f\"mean={vals.mean():.3f}\\nstd={vals.std():.3f}\",\n",
-    "        transform=ax.transAxes,\n",
-    "        fontsize=7,\n",
-    "        ha=\"right\",\n",
-    "        va=\"top\",\n",
-    "    )\n",
-    "\n",
-    "for j in range(i + 1, len(axes)):\n",
-    "    axes[j].axis(\"off\")\n",
-    "\n",
-    "fig.suptitle(\"Per-Encoder Activation Distributions\", fontsize=12, fontweight=\"bold\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Encoder Embedding Similarity (cosine)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Mean embedding per encoder (exclude hidden — different dim)\n",
-    "emb_names = [k for k in activations.keys() if k != \"hidden\"]\n",
-    "emb_means = torch.stack([activations[k].mean(dim=0) for k in emb_names])\n",
-    "emb_norm = F.normalize(emb_means, dim=1)\n",
-    "sim_matrix = (emb_norm @ emb_norm.T).cpu().numpy()\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(7, 6))\n",
-    "im = ax.imshow(sim_matrix, cmap=\"RdBu_r\", vmin=-1, vmax=1)\n",
-    "ax.set_xticks(range(len(emb_names)))\n",
-    "ax.set_yticks(range(len(emb_names)))\n",
-    "ax.set_xticklabels(emb_names, rotation=45, ha=\"right\", fontsize=8)\n",
-    "ax.set_yticklabels(emb_names, fontsize=8)\n",
-    "for i in range(len(emb_names)):\n",
-    "    for j in range(len(emb_names)):\n",
-    "        ax.text(j, i, f\"{sim_matrix[i, j]:.2f}\", ha=\"center\", va=\"center\", fontsize=7)\n",
-    "fig.colorbar(im, ax=ax)\n",
-    "ax.set_title(\"Cosine Similarity Between Encoder Mean Embeddings\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Architecture Comparison\n",
-    "Compare different architecture configs side-by-side without training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "configs = [\n",
-    "    {\"name\": \"tiny\", \"encoder_size\": 32, \"backbone_hidden_size\": 64},\n",
-    "    {\"name\": \"small\", \"encoder_size\": 64, \"backbone_hidden_size\": 128},\n",
-    "    {\"name\": \"medium\", \"encoder_size\": 128, \"backbone_hidden_size\": 256, \"backbone_num_layers\": 2},\n",
-    "    {\n",
-    "        \"name\": \"large\",\n",
-    "        \"encoder_size\": 128,\n",
-    "        \"backbone_hidden_size\": 512,\n",
-    "        \"backbone_num_layers\": 2,\n",
-    "        \"actor_num_layers\": 2,\n",
-    "        \"actor_hidden_size\": 256,\n",
-    "        \"critic_num_layers\": 2,\n",
-    "        \"critic_hidden_size\": 256,\n",
-    "    },\n",
-    "    {\n",
-    "        \"name\": \"xlarge\",\n",
-    "        \"encoder_size\": 256,\n",
-    "        \"backbone_hidden_size\": 1024,\n",
-    "        \"backbone_num_layers\": 3,\n",
-    "        \"actor_num_layers\": 2,\n",
-    "        \"actor_hidden_size\": 512,\n",
-    "        \"critic_num_layers\": 2,\n",
-    "        \"critic_hidden_size\": 512,\n",
-    "    },\n",
-    "    {\"name\": \"small+tanh\", \"encoder_size\": 64, \"backbone_hidden_size\": 128, \"encoder_activation\": \"tanh\"},\n",
-    "    {\n",
-    "        \"name\": \"medium+tanh\",\n",
-    "        \"encoder_size\": 128,\n",
-    "        \"backbone_hidden_size\": 256,\n",
-    "        \"backbone_num_layers\": 2,\n",
-    "        \"encoder_activation\": \"tanh\",\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "POLICY_DEFAULTS = {\n",
-    "    \"ego_input_size\": 64,\n",
-    "    \"partner_input_size\": 64,\n",
-    "    \"lane_input_size\": 64,\n",
-    "    \"boundary_input_size\": 64,\n",
-    "    \"traffic_control_input_size\": 64,\n",
-    "    \"target_input_size\": 64,\n",
-    "    \"backbone_num_layers\": 1,\n",
-    "    \"actor_hidden_size\": 128,\n",
-    "    \"actor_num_layers\": 0,\n",
-    "    \"critic_hidden_size\": 128,\n",
-    "    \"critic_num_layers\": 0,\n",
-    "    \"encoder_activation\": \"relu\",\n",
-    "    \"encoder_layer_norm\": True,\n",
-    "    \"backbone_activation\": \"gelu\",\n",
-    "    \"backbone_layer_norm\": False,\n",
-    "    \"shared_network\": True,\n",
-    "}\n",
-    "\n",
-    "results = []\n",
-    "for cfg in configs:\n",
-    "    name = cfg[\"name\"]\n",
-    "    encoder_size = cfg.get(\"encoder_size\", POLICY_DEFAULTS[\"ego_input_size\"])\n",
-    "    full_cfg = {**POLICY_DEFAULTS, **{k: v for k, v in cfg.items() if k not in (\"name\", \"encoder_size\")}}\n",
-    "    full_cfg.update(\n",
-    "        {\n",
-    "            \"ego_input_size\": encoder_size,\n",
-    "            \"partner_input_size\": encoder_size,\n",
-    "            \"lane_input_size\": encoder_size,\n",
-    "            \"boundary_input_size\": encoder_size,\n",
-    "            \"traffic_control_input_size\": encoder_size,\n",
-    "            \"target_input_size\": encoder_size,\n",
-    "        }\n",
-    "    )\n",
-    "    p = DrivePolicy(env, **full_cfg).to(device)\n",
-    "    n_params = sum(pp.numel() for pp in p.parameters())\n",
-    "\n",
-    "    with torch.no_grad():\n",
-    "        import time\n",
-    "\n",
-    "        t0 = time.time()\n",
-    "        for _ in range(100):\n",
-    "            p(obs_tensor)\n",
-    "        if device.type == \"cuda\":\n",
-    "            torch.cuda.synchronize()\n",
-    "        ms_per_fwd = (time.time() - t0) / 100 * 1000\n",
-    "\n",
-    "    results.append({\"name\": name, \"encoder_size\": encoder_size, \"params\": n_params, \"ms/fwd\": ms_per_fwd, **full_cfg})\n",
-    "    del p\n",
-    "\n",
-    "print(\n",
-    "    f\"{'Config':>12s} | {'enc':>5s} | {'bb_h':>5s} | {'bb_L':>4s} | {'act_h':>5s} | {'act_L':>5s} | {'crt_h':>5s} | {'crt_L':>5s} | {'enc_act':>7s} | {'Params':>10s} | {'ms/fwd':>8s}\"\n",
-    ")\n",
-    "print(\"-\" * 105)\n",
-    "for r in results:\n",
-    "    print(\n",
-    "        f\"{r['name']:>12s} | {r['encoder_size']:>5d} | {r['backbone_hidden_size']:>5d} | {r['backbone_num_layers']:>4d} | {r['actor_hidden_size']:>5d} | {r['actor_num_layers']:>5d} | {r['critic_hidden_size']:>5d} | {r['critic_num_layers']:>5d} | {r['encoder_activation']:>7s} | {r['params']:>10,d} | {r['ms/fwd']:>7.2f}ms\"\n",
-    "    )\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
-    "names = [r[\"name\"] for r in results]\n",
-    "params = [r[\"params\"] for r in results]\n",
-    "times = [r[\"ms/fwd\"] for r in results]\n",
-    "\n",
-    "bar_colors = [\"coral\" if r[\"encoder_activation\"] == \"tanh\" else \"steelblue\" for r in results]\n",
-    "\n",
-    "axes[0].bar(names, params, color=bar_colors, edgecolor=\"black\")\n",
-    "axes[0].set_ylabel(\"Parameters\")\n",
-    "axes[0].set_title(\"Parameter Count (orange=tanh encoder)\")\n",
-    "axes[0].tick_params(axis=\"x\", rotation=30)\n",
-    "for i, v in enumerate(params):\n",
-    "    axes[0].text(i, v, f\"{v:,}\", ha=\"center\", va=\"bottom\", fontsize=7)\n",
-    "\n",
-    "axes[1].bar(names, times, color=bar_colors, edgecolor=\"black\")\n",
-    "axes[1].set_ylabel(\"ms / forward\")\n",
-    "axes[1].set_title(f\"Forward Pass Latency ({env.num_agents} agents)\")\n",
-    "axes[1].tick_params(axis=\"x\", rotation=30)\n",
-    "for i, v in enumerate(times):\n",
-    "    axes[1].text(i, v, f\"{v:.2f}\", ha=\"center\", va=\"bottom\", fontsize=7)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Observation Buffer Utilization\n",
-    "How much of each observation slot is actually filled (non-zero)?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run a few steps to get diverse observations\n",
-    "actions = zero_actions(env)\n",
-    "all_obs = [obs]\n",
-    "for _ in range(20):\n",
-    "    o, _, _, _, _ = env.step(actions)\n",
-    "    all_obs.append(o)\n",
-    "stacked = np.concatenate(all_obs, axis=0)\n",
-    "\n",
-    "slide = env.ego_features\n",
-    "segments = [(\"ego\", 0, env.ego_features, 1, env.ego_features)]\n",
-    "if cond_dim > 0:\n",
-    "    segments.append((\"conditioning\", slide, slide + cond_dim, 1, cond_dim))\n",
-    "    slide += cond_dim\n",
-    "segments.append((\"partners\", slide, slide + partner_dim, env.obs_slots_partners_n, env.partner_features))\n",
-    "slide += partner_dim\n",
-    "segments.append((\"lanes\", slide, slide + lane_dim, env.obs_slots_lane_kept, env.road_features))\n",
-    "slide += lane_dim\n",
-    "segments.append((\"boundaries\", slide, slide + boundary_dim, env.obs_slots_boundary_kept, env.road_features))\n",
-    "slide += boundary_dim\n",
-    "segments.append((\"traffic\", slide, slide + traffic_dim, env.obs_slots_traffic_controls_n, env.traffic_control_features))\n",
-    "\n",
-    "print(f\"{'Segment':>15s} | {'Slots':>5s} | {'Features':>8s} | {'Fill %':>7s} | {'Mean':>8s} | {'Std':>8s}\")\n",
-    "print(\"-\" * 65)\n",
-    "fill_rates = []\n",
-    "seg_names = []\n",
-    "for name, start, end, n_slots, n_feat in segments:\n",
-    "    chunk = stacked[:, start:end]\n",
-    "    if n_slots > 1:\n",
-    "        reshaped = chunk.reshape(-1, n_slots, n_feat)\n",
-    "        # A slot is \"filled\" if any feature is non-zero\n",
-    "        filled = (np.abs(reshaped).sum(axis=2) > 1e-8).mean()\n",
-    "    else:\n",
-    "        filled = (np.abs(chunk) > 1e-8).mean()\n",
-    "    fill_rates.append(filled * 100)\n",
-    "    seg_names.append(name)\n",
-    "    print(f\"{name:>15s} | {n_slots:>5d} | {n_feat:>8d} | {filled:>6.1%} | {chunk.mean():>8.4f} | {chunk.std():>8.4f}\")\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "colors = [\"#2ecc71\" if f > 50 else \"#e74c3c\" if f < 10 else \"#f39c12\" for f in fill_rates]\n",
-    "ax.barh(seg_names, fill_rates, color=colors, edgecolor=\"black\")\n",
-    "ax.set_xlabel(\"Fill Rate (%)\")\n",
-    "ax.set_title(\"Observation Slot Utilization\")\n",
-    "ax.axvline(50, color=\"gray\", ls=\"--\", alpha=0.5)\n",
-    "for i, v in enumerate(fill_rates):\n",
-    "    ax.text(v + 1, i, f\"{v:.1f}%\", va=\"center\", fontsize=8)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Effective Receptive Field\n",
-    "Which input features have the most influence on the hidden representation?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Jacobian-based sensitivity: d(hidden) / d(obs) magnitude\n",
-    "sample = obs_tensor[:1].clone().requires_grad_(True)\n",
-    "hidden = policy.actor_backbone(sample, env.ego_features)\n",
-    "# Sum hidden to scalar for backward\n",
-    "hidden.sum().backward()\n",
-    "sensitivity = sample.grad.abs().squeeze().cpu().numpy()\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 1, figsize=(14, 6), gridspec_kw={\"height_ratios\": [2, 1]})\n",
-    "\n",
-    "# Full sensitivity\n",
-    "axes[0].plot(sensitivity, lw=0.5, color=\"steelblue\")\n",
-    "axes[0].set_ylabel(\"|grad|\")\n",
-    "axes[0].set_title(\"Input Feature Sensitivity (|d hidden / d obs|)\")\n",
-    "\n",
-    "# Mark segments\n",
-    "seg_boundaries = [0, env.ego_features]\n",
-    "seg_labels = [\"ego\"]\n",
-    "s = env.ego_features\n",
-    "if cond_dim > 0:\n",
-    "    s += cond_dim\n",
-    "    seg_boundaries.append(s)\n",
-    "    seg_labels.append(\"cond\")\n",
-    "for name, dim in [\n",
-    "    (\"partners\", partner_dim),\n",
-    "    (\"lanes\", lane_dim),\n",
-    "    (\"boundaries\", boundary_dim),\n",
-    "    (\"traffic\", traffic_dim),\n",
-    "]:\n",
-    "    s += dim\n",
-    "    seg_boundaries.append(s)\n",
-    "    seg_labels.append(name)\n",
-    "\n",
-    "seg_colors = plt.cm.Set2(np.linspace(0, 1, len(seg_labels)))\n",
-    "for i, (label, c) in enumerate(zip(seg_labels, seg_colors)):\n",
-    "    start, end = seg_boundaries[i], seg_boundaries[i + 1]\n",
-    "    axes[0].axvspan(start, end, alpha=0.15, color=c)\n",
-    "    axes[0].text((start + end) / 2, axes[0].get_ylim()[1] * 0.9, label, ha=\"center\", fontsize=7, color=\"black\")\n",
-    "\n",
-    "# Per-segment mean sensitivity\n",
-    "seg_means = []\n",
-    "for i in range(len(seg_labels)):\n",
-    "    start, end = seg_boundaries[i], seg_boundaries[i + 1]\n",
-    "    seg_means.append(sensitivity[start:end].mean())\n",
-    "\n",
-    "axes[1].bar(seg_labels, seg_means, color=seg_colors, edgecolor=\"black\")\n",
-    "axes[1].set_ylabel(\"Mean |grad|\")\n",
-    "axes[1].set_title(\"Mean Sensitivity per Observation Segment\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "policy.zero_grad()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/06_architecture.py b/notebooks/06_architecture.py
new file mode 100644
index 0000000000..f601fc7af2
--- /dev/null
+++ b/notebooks/06_architecture.py
@@ -0,0 +1,694 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.3
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # 06 - Neural Network Architecture
+# Visualize, analyze, and iterate on the DrivePolicy architecture. Covers model summary, per-encoder breakdown, forward pass shape tracing, weight distributions, and architecture comparison.
+
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.nn.functional as F
+from torchinfo import summary
+from pufferlib.ocean.drive import binding
+from pufferlib.ocean.torch import Drive as DrivePolicy
+from notebooks.notebook_utils import make_drive_env, zero_actions
+
+# --- Policy architecture ---
+INPUT_SIZE = 64
+BACKBONE_HIDDEN_SIZE = 1024
+BACKBONE_NUM_LAYERS = 3
+ACTOR_HIDDEN_SIZE = 128
+ACTOR_NUM_LAYERS = 3
+CRITIC_HIDDEN_SIZE = 64
+CRITIC_NUM_LAYERS = 2
+SHARED_NETWORK = True
+ENCODER_ACTIVATION = "tanh"
+ENCODER_LAYER_NORM = True
+BACKBONE_ACTIVATION = "gelu"
+BACKBONE_LAYER_NORM = False
+
+env, obs, info = make_drive_env()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+policy = DrivePolicy(
+    env,
+    ego_input_size=INPUT_SIZE,
+    partner_input_size=INPUT_SIZE,
+    lane_input_size=INPUT_SIZE,
+    boundary_input_size=INPUT_SIZE,
+    traffic_control_input_size=INPUT_SIZE,
+    target_input_size=INPUT_SIZE,
+    backbone_hidden_size=BACKBONE_HIDDEN_SIZE,
+    backbone_num_layers=BACKBONE_NUM_LAYERS,
+    actor_hidden_size=ACTOR_HIDDEN_SIZE,
+    actor_num_layers=ACTOR_NUM_LAYERS,
+    critic_hidden_size=CRITIC_HIDDEN_SIZE,
+    critic_num_layers=CRITIC_NUM_LAYERS,
+    encoder_activation=ENCODER_ACTIVATION,
+    encoder_layer_norm=ENCODER_LAYER_NORM,
+    backbone_activation=BACKBONE_ACTIVATION,
+    backbone_layer_norm=BACKBONE_LAYER_NORM,
+    shared_network=SHARED_NETWORK,
+).to(device)
+
+print(f"Device: {device}")
+print(f"Obs dim: {obs.shape[1]}")
+print(f"Action dim: {policy.atn_dim}")
+print(f"Shared network: {SHARED_NETWORK}")
+print(f"Backbone: {BACKBONE_HIDDEN_SIZE} x {BACKBONE_NUM_LAYERS}L")
+print(f"Actor: {ACTOR_HIDDEN_SIZE} x {ACTOR_NUM_LAYERS}L")
+print(f"Critic: {CRITIC_HIDDEN_SIZE} x {CRITIC_NUM_LAYERS}L")
+print(f"Encoder: {ENCODER_ACTIVATION}, LayerNorm: {ENCODER_LAYER_NORM}")
+
+# %% [markdown]
+# ## Model Summary (torchinfo)
+
+# %%
+obs_tensor = torch.FloatTensor(obs).to(device)
+summary(policy, input_data=obs_tensor, depth=4, col_names=["input_size", "output_size", "num_params", "mult_adds"])
+
+# %% [markdown]
+# ## Architecture Diagram
+
+# %%
+backbone = policy.actor_backbone
+cond_dim = backbone.target_dim
+
+# Collect encoder info
+encoders = [
+    ("ego", env.ego_features, 1, "direct", INPUT_SIZE),
+    ("conditioning", cond_dim, 1, "direct", INPUT_SIZE) if cond_dim > 0 else None,
+    ("partner", env.partner_features, env.obs_slots_partners_n, "max-pool", INPUT_SIZE),
+    ("lane", env.road_features, env.obs_slots_lane_kept, "max-pool", INPUT_SIZE),
+    ("boundary", env.road_features, env.obs_slots_boundary_kept, "max-pool", INPUT_SIZE),
+    (
+        "traffic_ctrl",
+        env.traffic_control_features - 2 + binding.NUM_TRAFFIC_CONTROL_TYPES + binding.NUM_TRAFFIC_CONTROL_STATES,
+        env.obs_slots_traffic_controls_n,
+        "max-pool (onehot)",
+        INPUT_SIZE,
+    ),
+]
+encoders = [e for e in encoders if e is not None]
+
+fig, ax = plt.subplots(figsize=(14, 8))
+ax.set_xlim(0, 10)
+ax.set_ylim(0, 10)
+ax.axis("off")
+
+n_enc = len(encoders)
+y_positions = np.linspace(9, 1, n_enc)
+colors = plt.cm.Set2(np.linspace(0, 1, n_enc))
+
+# Draw encoders
+for i, ((name, in_f, n_obj, agg, out_size), y, c) in enumerate(zip(encoders, y_positions, colors)):
+    # Input box
+    label = f"{name}\n{n_obj}x{in_f}" if n_obj > 1 else f"{name}\n{in_f}"
+    ax.add_patch(plt.Rectangle((0.2, y - 0.3), 1.6, 0.6, facecolor=c, edgecolor="black", lw=1.2, alpha=0.8))
+    ax.text(1.0, y, label, ha="center", va="center", fontsize=8, fontweight="bold")
+
+    # Encoder box
+    ax.add_patch(plt.Rectangle((2.5, y - 0.25), 2.0, 0.5, facecolor="lightyellow", edgecolor="black", lw=1))
+    ax.text(3.5, y + 0.05, f"Linear({in_f},{out_size})", ha="center", va="center", fontsize=7)
+    ln_label = "LN+" if ENCODER_LAYER_NORM else ""
+    ax.text(
+        3.5,
+        y - 0.12,
+        f"{ln_label}{ENCODER_ACTIVATION}+Linear({out_size},{out_size})",
+        ha="center",
+        va="center",
+        fontsize=6,
+        color="gray",
+    )
+
+    # Aggregation
+    if n_obj > 1:
+        ax.text(5.0, y, agg, ha="center", va="center", fontsize=7, style="italic", color="darkblue")
+        arrow_start = 5.5
+    else:
+        arrow_start = 4.6
+
+    # Arrows
+    ax.annotate("", xy=(2.5, y), xytext=(1.8, y), arrowprops=dict(arrowstyle="->", lw=1))
+    ax.annotate("", xy=(6.0, 5.0), xytext=(arrow_start, y), arrowprops=dict(arrowstyle="->", lw=0.8, color="gray"))
+
+# Concat box
+ax.add_patch(plt.Rectangle((5.8, 4.5), 1.4, 1.0, facecolor="lightsalmon", edgecolor="black", lw=1.5))
+ax.text(6.5, 5.2, "Concat", ha="center", va="center", fontsize=9, fontweight="bold")
+ax.text(6.5, 4.85, f"{n_enc}x{INPUT_SIZE}={n_enc * INPUT_SIZE}", ha="center", va="center", fontsize=7)
+
+# Backbone
+ax.add_patch(plt.Rectangle((7.5, 4.5), 1.3, 1.0, facecolor="lightblue", edgecolor="black", lw=1.5))
+ax.text(8.15, 5.15, f"Backbone ({BACKBONE_NUM_LAYERS}L)", ha="center", va="center", fontsize=8, fontweight="bold")
+ax.text(8.15, 4.85, f"GELU+Linear\n({n_enc * INPUT_SIZE},{BACKBONE_HIDDEN_SIZE})", ha="center", va="center", fontsize=6)
+ax.annotate("", xy=(7.5, 5.0), xytext=(7.2, 5.0), arrowprops=dict(arrowstyle="->", lw=1.5))
+
+# Actor / Critic heads
+ax.add_patch(plt.Rectangle((9.0, 5.7), 0.9, 0.6, facecolor="lightgreen", edgecolor="black", lw=1.2))
+actor_label = f"Actor ({ACTOR_NUM_LAYERS}L)\n{BACKBONE_HIDDEN_SIZE}->{sum(policy.atn_dim)}"
+if ACTOR_NUM_LAYERS > 1:
+    actor_label = (
+        f"Actor ({ACTOR_NUM_LAYERS}L)\n{BACKBONE_HIDDEN_SIZE}->{ACTOR_HIDDEN_SIZE}->...->{sum(policy.atn_dim)}"
+    )
+ax.text(9.45, 6.0, actor_label, ha="center", va="center", fontsize=6, fontweight="bold")
+
+ax.add_patch(plt.Rectangle((9.0, 3.7), 0.9, 0.6, facecolor="plum", edgecolor="black", lw=1.2))
+critic_label = f"Critic ({CRITIC_NUM_LAYERS}L)\n{BACKBONE_HIDDEN_SIZE}->1"
+if CRITIC_NUM_LAYERS > 1:
+    critic_label = f"Critic ({CRITIC_NUM_LAYERS}L)\n{BACKBONE_HIDDEN_SIZE}->{CRITIC_HIDDEN_SIZE}->...->1"
+ax.text(9.45, 4.0, critic_label, ha="center", va="center", fontsize=6, fontweight="bold")
+
+ax.annotate("", xy=(9.0, 6.0), xytext=(8.8, 5.3), arrowprops=dict(arrowstyle="->", lw=1.2))
+ax.annotate("", xy=(9.0, 4.0), xytext=(8.8, 4.7), arrowprops=dict(arrowstyle="->", lw=1.2))
+
+split_label = "SHARED" if SHARED_NETWORK else "SPLIT"
+ax.text(8.9, 4.55, split_label, ha="center", va="center", fontsize=7, color="red", fontweight="bold")
+
+ax.text(
+    5.0,
+    0.3,
+    f"Encoder: {ENCODER_ACTIVATION} | LayerNorm: {ENCODER_LAYER_NORM}",
+    ha="center",
+    va="center",
+    fontsize=8,
+    color="darkgreen",
+    fontweight="bold",
+)
+
+ax.set_title(
+    f"DrivePolicy Architecture (encoder_size={INPUT_SIZE}, backbone={BACKBONE_HIDDEN_SIZE})",
+    fontsize=12,
+    fontweight="bold",
+)
+plt.tight_layout()
+plt.show()
+
+
+# %% [markdown]
+# ## Per-Encoder Parameter Breakdown
+
+
+# %%
+def count_params(module):
+    return sum(p.numel() for p in module.parameters())
+
+
+backbone = policy.actor_backbone
+components = {
+    "ego_encoder": backbone.ego_encoder,
+    "lane_encoder": backbone.lane_encoder,
+    "boundary_encoder": backbone.boundary_encoder,
+    "partner_encoder": backbone.partner_encoder,
+    "traffic_ctrl_encoder": backbone.traffic_control_encoder,
+}
+if backbone.target_dim > 0:
+    components["target_encoder"] = backbone.target_encoder
+components["backbone_mlp"] = backbone.backbone
+components["actor_head"] = policy.actor_head
+components["critic_head"] = policy.critic_head
+
+names, counts = zip(*[(k, count_params(v)) for k, v in components.items()])
+total = sum(counts)
+
+print(f"{'Component':>25s} | {'Params':>10s} | {'%':>6s}")
+print("-" * 48)
+for n, c in zip(names, counts):
+    print(f"{n:>25s} | {c:>10,d} | {c / total:>5.1%}")
+print("-" * 48)
+print(f"{'TOTAL':>25s} | {total:>10,d}")
+if not SHARED_NETWORK:
+    critic_bb = count_params(policy.critic_backbone)
+    print(f"{'+ critic_backbone':>25s} | {critic_bb:>10,d}")
+    print(f"{'GRAND TOTAL':>25s} | {total + critic_bb:>10,d}")
+
+fig, ax = plt.subplots(figsize=(8, 5))
+colors = plt.cm.Set3(np.linspace(0, 1, len(names)))
+bars = ax.barh(names, counts, color=colors, edgecolor="black")
+for bar, c in zip(bars, counts):
+    ax.text(bar.get_width() + total * 0.01, bar.get_y() + bar.get_height() / 2, f"{c:,}", va="center", fontsize=8)
+ax.set_xlabel("Parameters")
+ax.set_title(f"Parameter Distribution ({total:,} total)")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Forward Pass Shape Trace
+
+# %%
+x = obs_tensor
+backbone = policy.actor_backbone
+
+slide_idx = env.ego_features
+cond_dim = backbone.target_dim
+partner_dim = env.obs_slots_partners_n * env.partner_features
+lane_dim = env.obs_slots_lane_kept * env.road_features
+boundary_dim = env.obs_slots_boundary_kept * env.road_features
+traffic_dim = env.obs_slots_traffic_controls_n * env.traffic_control_features
+
+# Slicing
+ego_obs = x[:, :slide_idx]
+slices = [("ego", 0, slide_idx, ego_obs.shape)]
+
+if cond_dim > 0:
+    cond_obs = x[:, slide_idx : slide_idx + cond_dim]
+    slices.append(("conditioning", slide_idx, slide_idx + cond_dim, cond_obs.shape))
+    slide_idx += cond_dim
+
+partner_obs = x[:, slide_idx : slide_idx + partner_dim]
+slices.append(("partners", slide_idx, slide_idx + partner_dim, partner_obs.shape))
+slide_idx += partner_dim
+
+lane_obs = x[:, slide_idx : slide_idx + lane_dim]
+slices.append(("lanes", slide_idx, slide_idx + lane_dim, lane_obs.shape))
+slide_idx += lane_dim
+
+boundary_obs = x[:, slide_idx : slide_idx + boundary_dim]
+slices.append(("boundaries", slide_idx, slide_idx + boundary_dim, boundary_obs.shape))
+slide_idx += boundary_dim
+
+traffic_obs = x[:, slide_idx : slide_idx + traffic_dim]
+slices.append(("traffic_ctrl", slide_idx, slide_idx + traffic_dim, traffic_obs.shape))
+
+print(f"Obs buffer layout (total={x.shape[1]}):")
+print(f"{'Name':>15s} | {'Start':>5s} | {'End':>5s} | {'Width':>5s} | Shape")
+print("-" * 65)
+for name, start, end, shape in slices:
+    print(f"{name:>15s} | {start:>5d} | {end:>5d} | {end - start:>5d} | {shape}")
+
+# Forward through encoders
+print("\nEncoder outputs:")
+with torch.no_grad():
+    ego_enc = backbone.ego_encoder(ego_obs)
+    print(f"  ego_encoder:     {ego_obs.shape} -> {ego_enc.shape}")
+
+    if cond_dim > 0:
+        cond_enc = backbone.target_encoder(cond_obs)
+        print(f"  cond_encoder:    {cond_obs.shape} -> {cond_enc.shape}")
+
+    p_reshaped = partner_obs.view(-1, env.obs_slots_partners_n, env.partner_features)
+    p_enc, _ = backbone.partner_encoder(p_reshaped).max(dim=1)
+    print(f"  partner_encoder: {partner_obs.shape} -> view {p_reshaped.shape} -> encode -> max-pool -> {p_enc.shape}")
+
+    l_reshaped = lane_obs.view(-1, env.obs_slots_lane_kept, env.road_features)
+    l_enc, _ = backbone.lane_encoder(l_reshaped).max(dim=1)
+    print(f"  lane_encoder:    {lane_obs.shape} -> view {l_reshaped.shape} -> encode -> max-pool -> {l_enc.shape}")
+
+    b_reshaped = boundary_obs.view(-1, env.obs_slots_boundary_kept, env.road_features)
+    b_enc, _ = backbone.boundary_encoder(b_reshaped).max(dim=1)
+    print(f"  bound_encoder:   {boundary_obs.shape} -> view {b_reshaped.shape} -> encode -> max-pool -> {b_enc.shape}")
+
+    t_reshaped = traffic_obs.view(-1, env.obs_slots_traffic_controls_n, env.traffic_control_features)
+    t_cont = t_reshaped[:, :, : env.traffic_control_features - 2]
+    t_type = t_reshaped[:, :, env.traffic_control_features - 2]
+    t_state = t_reshaped[:, :, env.traffic_control_features - 1]
+    t_type_onehot = F.one_hot(t_type.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_TYPES).float()
+    t_state_onehot = F.one_hot(t_state.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_STATES).float()
+    t_input = torch.cat([t_cont, t_type_onehot, t_state_onehot], dim=2)
+    t_enc, _ = backbone.traffic_control_encoder(t_input).max(dim=1)
+    print(
+        f"  traffic_encoder: {traffic_obs.shape} -> view {t_reshaped.shape} -> onehot {t_input.shape} -> encode -> max-pool -> {t_enc.shape}"
+    )
+
+    # Concat + backbone
+    features = [ego_enc, l_enc, b_enc, p_enc, t_enc]
+    if cond_dim > 0:
+        features.append(cond_enc)
+    concat = torch.cat(features, dim=1)
+    hidden = backbone.backbone(concat)
+    print(f"\n  concat: {concat.shape}")
+    print(f"  backbone_mlp: {concat.shape} -> {hidden.shape}")
+
+    # Heads
+    actor_out = policy.actor_head(hidden)
+    critic_out = policy.critic_head(hidden)
+    print(f"  actor_head:  {hidden.shape} -> {actor_out.shape} (split into {policy.atn_dim})")
+    print(f"  critic_head: {hidden.shape} -> {critic_out.shape}")
+
+# %% [markdown]
+# ## Weight Distributions by Layer
+
+# %%
+weight_data = [
+    (n, p.data.cpu().numpy().flatten()) for n, p in policy.named_parameters() if "weight" in n and p.dim() >= 2
+]
+
+n_weights = len(weight_data)
+cols = 4
+rows = (n_weights + cols - 1) // cols
+fig, axes = plt.subplots(rows, cols, figsize=(4 * cols, 3 * rows))
+axes = axes.flatten()
+
+for i, (name, w) in enumerate(weight_data):
+    ax = axes[i]
+    ax.hist(w, bins=50, edgecolor="black", alpha=0.7, density=True)
+    ax.set_title(name.replace("actor_backbone.", ""), fontsize=7)
+    ax.axvline(0, color="red", ls="--", lw=0.5)
+    ax.text(0.95, 0.95, f"std={w.std():.3f}", transform=ax.transAxes, fontsize=6, ha="right", va="top")
+
+for j in range(i + 1, len(axes)):
+    axes[j].axis("off")
+
+fig.suptitle("Weight Distributions (init)", fontsize=12, fontweight="bold")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Activation Analysis (per encoder)
+
+# %%
+policy.eval()
+with torch.no_grad():
+    hidden = policy.actor_backbone(obs_tensor, env.ego_features)
+    action_logits, value = policy.decode_actions(hidden)
+
+# Collect per-encoder activations
+activations = {}
+with torch.no_grad():
+    slide = env.ego_features
+    activations["ego"] = backbone.ego_encoder(obs_tensor[:, : env.ego_features])
+
+    if cond_dim > 0:
+        activations["conditioning"] = backbone.target_encoder(obs_tensor[:, slide : slide + cond_dim])
+        slide += cond_dim
+
+    p_obs = obs_tensor[:, slide : slide + partner_dim].view(-1, env.obs_slots_partners_n, env.partner_features)
+    activations["partner"], _ = backbone.partner_encoder(p_obs).max(dim=1)
+    slide += partner_dim
+
+    l_obs = obs_tensor[:, slide : slide + lane_dim].view(-1, env.obs_slots_lane_kept, env.road_features)
+    activations["lane"], _ = backbone.lane_encoder(l_obs).max(dim=1)
+    slide += lane_dim
+
+    b_obs = obs_tensor[:, slide : slide + boundary_dim].view(-1, env.obs_slots_boundary_kept, env.road_features)
+    activations["boundary"], _ = backbone.boundary_encoder(b_obs).max(dim=1)
+    slide += boundary_dim
+
+    t_obs = obs_tensor[:, slide : slide + traffic_dim].view(
+        -1, env.obs_slots_traffic_controls_n, env.traffic_control_features
+    )
+    t_cont = t_obs[:, :, : env.traffic_control_features - 2]
+    t_type = t_obs[:, :, env.traffic_control_features - 2]
+    t_state = t_obs[:, :, env.traffic_control_features - 1]
+    t_type_onehot = F.one_hot(t_type.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_TYPES).float()
+    t_state_onehot = F.one_hot(t_state.long(), num_classes=binding.NUM_TRAFFIC_CONTROL_STATES).float()
+    t_input = torch.cat([t_cont, t_type_onehot, t_state_onehot], dim=2)
+    activations["traffic_ctrl"], _ = backbone.traffic_control_encoder(t_input).max(dim=1)
+
+    activations["hidden"] = hidden
+
+fig, axes = plt.subplots(2, 4, figsize=(16, 6))
+axes = axes.flatten()
+for i, (name, act) in enumerate(activations.items()):
+    if i >= len(axes):
+        break
+    vals = act.cpu().numpy().flatten()
+    ax = axes[i]
+    ax.hist(vals, bins=50, edgecolor="black", alpha=0.7)
+    dead = (act.abs().sum(dim=0) == 0).sum().item()
+    ax.set_title(f"{name} (dead={dead}/{act.shape[1]})", fontsize=9)
+    ax.text(
+        0.95,
+        0.95,
+        f"mean={vals.mean():.3f}\nstd={vals.std():.3f}",
+        transform=ax.transAxes,
+        fontsize=7,
+        ha="right",
+        va="top",
+    )
+
+for j in range(i + 1, len(axes)):
+    axes[j].axis("off")
+
+fig.suptitle("Per-Encoder Activation Distributions", fontsize=12, fontweight="bold")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Encoder Embedding Similarity (cosine)
+
+# %%
+# Mean embedding per encoder (exclude hidden — different dim)
+emb_names = [k for k in activations.keys() if k != "hidden"]
+emb_means = torch.stack([activations[k].mean(dim=0) for k in emb_names])
+emb_norm = F.normalize(emb_means, dim=1)
+sim_matrix = (emb_norm @ emb_norm.T).cpu().numpy()
+
+fig, ax = plt.subplots(figsize=(7, 6))
+im = ax.imshow(sim_matrix, cmap="RdBu_r", vmin=-1, vmax=1)
+ax.set_xticks(range(len(emb_names)))
+ax.set_yticks(range(len(emb_names)))
+ax.set_xticklabels(emb_names, rotation=45, ha="right", fontsize=8)
+ax.set_yticklabels(emb_names, fontsize=8)
+for i in range(len(emb_names)):
+    for j in range(len(emb_names)):
+        ax.text(j, i, f"{sim_matrix[i, j]:.2f}", ha="center", va="center", fontsize=7)
+fig.colorbar(im, ax=ax)
+ax.set_title("Cosine Similarity Between Encoder Mean Embeddings")
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Architecture Comparison
+# Compare different architecture configs side-by-side without training.
+
+# %%
+configs = [
+    {"name": "tiny", "encoder_size": 32, "backbone_hidden_size": 64},
+    {"name": "small", "encoder_size": 64, "backbone_hidden_size": 128},
+    {"name": "medium", "encoder_size": 128, "backbone_hidden_size": 256, "backbone_num_layers": 2},
+    {
+        "name": "large",
+        "encoder_size": 128,
+        "backbone_hidden_size": 512,
+        "backbone_num_layers": 2,
+        "actor_num_layers": 2,
+        "actor_hidden_size": 256,
+        "critic_num_layers": 2,
+        "critic_hidden_size": 256,
+    },
+    {
+        "name": "xlarge",
+        "encoder_size": 256,
+        "backbone_hidden_size": 1024,
+        "backbone_num_layers": 3,
+        "actor_num_layers": 2,
+        "actor_hidden_size": 512,
+        "critic_num_layers": 2,
+        "critic_hidden_size": 512,
+    },
+    {"name": "small+tanh", "encoder_size": 64, "backbone_hidden_size": 128, "encoder_activation": "tanh"},
+    {
+        "name": "medium+tanh",
+        "encoder_size": 128,
+        "backbone_hidden_size": 256,
+        "backbone_num_layers": 2,
+        "encoder_activation": "tanh",
+    },
+]
+
+POLICY_DEFAULTS = {
+    "ego_input_size": 64,
+    "partner_input_size": 64,
+    "lane_input_size": 64,
+    "boundary_input_size": 64,
+    "traffic_control_input_size": 64,
+    "target_input_size": 64,
+    "backbone_num_layers": 1,
+    "actor_hidden_size": 128,
+    "actor_num_layers": 0,
+    "critic_hidden_size": 128,
+    "critic_num_layers": 0,
+    "encoder_activation": "relu",
+    "encoder_layer_norm": True,
+    "backbone_activation": "gelu",
+    "backbone_layer_norm": False,
+    "shared_network": True,
+}
+
+results = []
+for cfg in configs:
+    name = cfg["name"]
+    encoder_size = cfg.get("encoder_size", POLICY_DEFAULTS["ego_input_size"])
+    full_cfg = {**POLICY_DEFAULTS, **{k: v for k, v in cfg.items() if k not in ("name", "encoder_size")}}
+    full_cfg.update(
+        {
+            "ego_input_size": encoder_size,
+            "partner_input_size": encoder_size,
+            "lane_input_size": encoder_size,
+            "boundary_input_size": encoder_size,
+            "traffic_control_input_size": encoder_size,
+            "target_input_size": encoder_size,
+        }
+    )
+    p = DrivePolicy(env, **full_cfg).to(device)
+    n_params = sum(pp.numel() for pp in p.parameters())
+
+    with torch.no_grad():
+        import time
+
+        t0 = time.time()
+        for _ in range(100):
+            p(obs_tensor)
+        if device.type == "cuda":
+            torch.cuda.synchronize()
+        ms_per_fwd = (time.time() - t0) / 100 * 1000
+
+    results.append({"name": name, "encoder_size": encoder_size, "params": n_params, "ms/fwd": ms_per_fwd, **full_cfg})
+    del p
+
+print(
+    f"{'Config':>12s} | {'enc':>5s} | {'bb_h':>5s} | {'bb_L':>4s} | {'act_h':>5s} | {'act_L':>5s} | {'crt_h':>5s} | {'crt_L':>5s} | {'enc_act':>7s} | {'Params':>10s} | {'ms/fwd':>8s}"
+)
+print("-" * 105)
+for r in results:
+    print(
+        f"{r['name']:>12s} | {r['encoder_size']:>5d} | {r['backbone_hidden_size']:>5d} | {r['backbone_num_layers']:>4d} | {r['actor_hidden_size']:>5d} | {r['actor_num_layers']:>5d} | {r['critic_hidden_size']:>5d} | {r['critic_num_layers']:>5d} | {r['encoder_activation']:>7s} | {r['params']:>10,d} | {r['ms/fwd']:>7.2f}ms"
+    )
+
+fig, axes = plt.subplots(1, 2, figsize=(14, 4))
+names = [r["name"] for r in results]
+params = [r["params"] for r in results]
+times = [r["ms/fwd"] for r in results]
+
+bar_colors = ["coral" if r["encoder_activation"] == "tanh" else "steelblue" for r in results]
+
+axes[0].bar(names, params, color=bar_colors, edgecolor="black")
+axes[0].set_ylabel("Parameters")
+axes[0].set_title("Parameter Count (orange=tanh encoder)")
+axes[0].tick_params(axis="x", rotation=30)
+for i, v in enumerate(params):
+    axes[0].text(i, v, f"{v:,}", ha="center", va="bottom", fontsize=7)
+
+axes[1].bar(names, times, color=bar_colors, edgecolor="black")
+axes[1].set_ylabel("ms / forward")
+axes[1].set_title(f"Forward Pass Latency ({env.num_agents} agents)")
+axes[1].tick_params(axis="x", rotation=30)
+for i, v in enumerate(times):
+    axes[1].text(i, v, f"{v:.2f}", ha="center", va="bottom", fontsize=7)
+
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Observation Buffer Utilization
+# How much of each observation slot is actually filled (non-zero)?
+
+# %%
+# Run a few steps to get diverse observations
+actions = zero_actions(env)
+all_obs = [obs]
+for _ in range(20):
+    o, _, _, _, _ = env.step(actions)
+    all_obs.append(o)
+stacked = np.concatenate(all_obs, axis=0)
+
+slide = env.ego_features
+segments = [("ego", 0, env.ego_features, 1, env.ego_features)]
+if cond_dim > 0:
+    segments.append(("conditioning", slide, slide + cond_dim, 1, cond_dim))
+    slide += cond_dim
+segments.append(("partners", slide, slide + partner_dim, env.obs_slots_partners_n, env.partner_features))
+slide += partner_dim
+segments.append(("lanes", slide, slide + lane_dim, env.obs_slots_lane_kept, env.road_features))
+slide += lane_dim
+segments.append(("boundaries", slide, slide + boundary_dim, env.obs_slots_boundary_kept, env.road_features))
+slide += boundary_dim
+segments.append(("traffic", slide, slide + traffic_dim, env.obs_slots_traffic_controls_n, env.traffic_control_features))
+
+print(f"{'Segment':>15s} | {'Slots':>5s} | {'Features':>8s} | {'Fill %':>7s} | {'Mean':>8s} | {'Std':>8s}")
+print("-" * 65)
+fill_rates = []
+seg_names = []
+for name, start, end, n_slots, n_feat in segments:
+    chunk = stacked[:, start:end]
+    if n_slots > 1:
+        reshaped = chunk.reshape(-1, n_slots, n_feat)
+        # A slot is "filled" if any feature is non-zero
+        filled = (np.abs(reshaped).sum(axis=2) > 1e-8).mean()
+    else:
+        filled = (np.abs(chunk) > 1e-8).mean()
+    fill_rates.append(filled * 100)
+    seg_names.append(name)
+    print(f"{name:>15s} | {n_slots:>5d} | {n_feat:>8d} | {filled:>6.1%} | {chunk.mean():>8.4f} | {chunk.std():>8.4f}")
+
+fig, ax = plt.subplots(figsize=(8, 4))
+colors = ["#2ecc71" if f > 50 else "#e74c3c" if f < 10 else "#f39c12" for f in fill_rates]
+ax.barh(seg_names, fill_rates, color=colors, edgecolor="black")
+ax.set_xlabel("Fill Rate (%)")
+ax.set_title("Observation Slot Utilization")
+ax.axvline(50, color="gray", ls="--", alpha=0.5)
+for i, v in enumerate(fill_rates):
+    ax.text(v + 1, i, f"{v:.1f}%", va="center", fontsize=8)
+plt.tight_layout()
+plt.show()
+
+# %% [markdown]
+# ## Effective Receptive Field
+# Which input features have the most influence on the hidden representation?
+
+# %%
+# Jacobian-based sensitivity: d(hidden) / d(obs) magnitude
+sample = obs_tensor[:1].clone().requires_grad_(True)
+hidden = policy.actor_backbone(sample, env.ego_features)
+# Sum hidden to scalar for backward
+hidden.sum().backward()
+sensitivity = sample.grad.abs().squeeze().cpu().numpy()
+
+fig, axes = plt.subplots(2, 1, figsize=(14, 6), gridspec_kw={"height_ratios": [2, 1]})
+
+# Full sensitivity
+axes[0].plot(sensitivity, lw=0.5, color="steelblue")
+axes[0].set_ylabel("|grad|")
+axes[0].set_title("Input Feature Sensitivity (|d hidden / d obs|)")
+
+# Mark segments
+seg_boundaries = [0, env.ego_features]
+seg_labels = ["ego"]
+s = env.ego_features
+if cond_dim > 0:
+    s += cond_dim
+    seg_boundaries.append(s)
+    seg_labels.append("cond")
+for name, dim in [
+    ("partners", partner_dim),
+    ("lanes", lane_dim),
+    ("boundaries", boundary_dim),
+    ("traffic", traffic_dim),
+]:
+    s += dim
+    seg_boundaries.append(s)
+    seg_labels.append(name)
+
+seg_colors = plt.cm.Set2(np.linspace(0, 1, len(seg_labels)))
+for i, (label, c) in enumerate(zip(seg_labels, seg_colors)):
+    start, end = seg_boundaries[i], seg_boundaries[i + 1]
+    axes[0].axvspan(start, end, alpha=0.15, color=c)
+    axes[0].text((start + end) / 2, axes[0].get_ylim()[1] * 0.9, label, ha="center", fontsize=7, color="black")
+
+# Per-segment mean sensitivity
+seg_means = []
+for i in range(len(seg_labels)):
+    start, end = seg_boundaries[i], seg_boundaries[i + 1]
+    seg_means.append(sensitivity[start:end].mean())
+
+axes[1].bar(seg_labels, seg_means, color=seg_colors, edgecolor="black")
+axes[1].set_ylabel("Mean |grad|")
+axes[1].set_title("Mean Sensitivity per Observation Segment")
+
+plt.tight_layout()
+plt.show()
+
+policy.zero_grad()

From 0961e3af6c6d3d24cf2c3726a5149501e138930d Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Wed, 3 Jun 2026 17:39:04 +0200
Subject: [PATCH 08/19] Rename target_dim to goal_dim for clarity in Drive
 class

---
 pufferlib/ocean/drive/drive.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py
index 370425ed29..214935550d 100644
--- a/pufferlib/ocean/drive/drive.py
+++ b/pufferlib/ocean/drive/drive.py
@@ -220,12 +220,12 @@ def __init__(
             self.target_features = binding.STATIC_TARGET_FEATURES
         else:
             self.target_features = binding.DYNAMIC_TARGET_FEATURES
-        self.target_dim = self.num_target_waypoints * self.target_features
+        self.goal_dim = self.num_target_waypoints * self.target_features
 
         self.num_obs = (
             self.ego_features
             + self.num_reward_coefs
-            + self.target_dim
+            + self.goal_dim
             + self.obs_slots_partners_n * self.partner_features
             + self.obs_slots_lane_kept * self.road_features
             + self.obs_slots_boundary_kept * self.road_features

From 03cc5384f0ec7c8ac2d80e7d971ded81a0aa2bd6 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Wed, 3 Jun 2026 18:09:42 +0200
Subject: [PATCH 09/19] Update inference and architecture notebooks to include
 mask_padded_features parameter

---
 notebooks/05_inference.py    | 5 ++++-
 notebooks/06_architecture.py | 3 +++
 notebooks/notebook_utils.py  | 1 +
 3 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/notebooks/05_inference.py b/notebooks/05_inference.py
index 8a2ffc8013..eddcc1ad8e 100644
--- a/notebooks/05_inference.py
+++ b/notebooks/05_inference.py
@@ -801,7 +801,7 @@ def unpack_all_timesteps(bufs, agent_idx):
     f"({100 * len(visible_partners) / (all_partners.shape[0] * obs_slots_partners_n):.1f}%)"
 )
 
-fig, axes = plt.subplots(2, 5, figsize=(21, 8))
+fig, axes = plt.subplots(3, 4, figsize=(21, 11))
 axes = axes.flatten()
 
 for i, label in enumerate(partner_labels):
@@ -821,6 +821,9 @@ def unpack_all_timesteps(bufs, agent_idx):
 pos_ax.set_aspect("equal")
 pos_ax.grid(True, alpha=0.3)
 
+for ax in axes[len(partner_labels) + 1 :]:
+    ax.axis("off")
+
 fig.suptitle("Partner features: all visible, full rollout", fontsize=13)
 plt.tight_layout()
 plt.show()
diff --git a/notebooks/06_architecture.py b/notebooks/06_architecture.py
index f601fc7af2..ddcd8eac41 100644
--- a/notebooks/06_architecture.py
+++ b/notebooks/06_architecture.py
@@ -39,6 +39,7 @@
 ENCODER_LAYER_NORM = True
 BACKBONE_ACTIVATION = "gelu"
 BACKBONE_LAYER_NORM = False
+MASK_PADDED_FEATURES = False
 
 env, obs, info = make_drive_env()
 
@@ -62,6 +63,7 @@
     backbone_activation=BACKBONE_ACTIVATION,
     backbone_layer_norm=BACKBONE_LAYER_NORM,
     shared_network=SHARED_NETWORK,
+    mask_padded_features=MASK_PADDED_FEATURES,
 ).to(device)
 
 print(f"Device: {device}")
@@ -516,6 +518,7 @@ def count_params(module):
     "backbone_activation": "gelu",
     "backbone_layer_norm": False,
     "shared_network": True,
+    "mask_padded_features": False,
 }
 
 results = []
diff --git a/notebooks/notebook_utils.py b/notebooks/notebook_utils.py
index cbd13bfcc1..815cc58ee2 100644
--- a/notebooks/notebook_utils.py
+++ b/notebooks/notebook_utils.py
@@ -109,6 +109,7 @@
     "backbone_activation": "gelu",
     "backbone_layer_norm": False,
     "shared_network": True,
+    "mask_padded_features": False,
 }
 
 

From 4e58499c3da510d92b4a83585156ccd500324946 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Wed, 3 Jun 2026 18:17:25 +0200
Subject: [PATCH 10/19] Update CI configuration and test data for improved
 performance and consistency

---
 .github/workflows/ci.yml                      |  2 +-
 setup.py                                      |  1 +
 .../smoke_tests/data/drive_smoke_golden.json  | 60 +++++++++----------
 3 files changed, 32 insertions(+), 31 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 4d239b9a4a..5927bf2f08 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -66,7 +66,7 @@ jobs:
           PIP_NO_CACHE_DIR: 1
         run: |
           sudo apt-get update && sudo apt-get install -y build-essential cmake
-          python -m pip install -U pip pytest jupytext nbclient ipykernel ipywidgets
+          python -m pip install -U pip pytest jupytext nbclient ipykernel
           pip install -e . --no-cache-dir
           python setup.py build_ext --inplace --force
 
diff --git a/setup.py b/setup.py
index 7504107dd2..52b5283cdc 100644
--- a/setup.py
+++ b/setup.py
@@ -277,6 +277,7 @@ def run(self):
         "tensorboard",
         "jupytext",
         "torchinfo",
+        "ipywidgets",
     ]
 
 setup(
diff --git a/tests/smoke_tests/data/drive_smoke_golden.json b/tests/smoke_tests/data/drive_smoke_golden.json
index b3e2c6865e..01734a546f 100644
--- a/tests/smoke_tests/data/drive_smoke_golden.json
+++ b/tests/smoke_tests/data/drive_smoke_golden.json
@@ -1,48 +1,48 @@
 {
   "env": {
-    "avg_distance_per_infraction": 13.18341121673584,
-    "avg_speed_per_agent": 1.3711345672607422,
-    "collision_rate": 0.0375,
-    "comfort_violation_count": 0.7263497829437255,
+    "avg_distance_per_infraction": 14.85188889503479,
+    "avg_speed_per_agent": 1.4011926352977753,
+    "collision_rate": 0.0,
+    "comfort_violation_count": 0.7380953431129456,
     "dnf_rate": 0.5625,
-    "episode_length": 41.8,
-    "episode_return": -2.334346318244934,
-    "lane_center_rate": 0.7175199031829834,
+    "episode_length": 46.0,
+    "episode_return": -2.523432493209839,
+    "lane_center_rate": 0.705732524394989,
     "n": 16.0,
     "num_goals_reached": 0.0,
     "obs/max": 80.0,
-    "obs/mean": 0.2281341243069619,
-    "obs/min": -1.236907229758799,
-    "offroad_rate": 0.375,
-    "red_light_violation_rate": 0.025,
+    "obs/mean": 0.22964997438248247,
+    "obs/min": -1.1160968635231256,
+    "offroad_rate": 0.40625,
+    "red_light_violation_rate": 0.03125,
     "reward_components/ade": 0.0,
-    "reward_components/collision": -0.06195625364780426,
-    "reward_components/comfort": -1.5356246471405028,
+    "reward_components/collision": 0.0,
+    "reward_components/comfort": -1.712499350309372,
     "reward_components/goal": 0.0,
-    "reward_components/lane_align": -0.13004764765501023,
-    "reward_components/lane_center": -0.002508045267313719,
-    "reward_components/offroad": -0.5625,
+    "reward_components/lane_align": -0.14824828505516052,
+    "reward_components/lane_center": -0.003005934282555245,
+    "reward_components/offroad": -0.609375,
     "reward_components/overspeed": 0.0,
-    "reward_components/red_light": -0.025,
-    "reward_components/reverse": -0.01661874633282423,
-    "reward_components/timestep": -9.165622614091263e-05,
+    "reward_components/red_light": -0.03125,
+    "reward_components/reverse": -0.018953118938952684,
+    "reward_components/timestep": -0.00010140621816390194,
     "reward_components/velocity": 0.0,
     "score": 0.0,
     "velocity_progress_sum": 0.0
   },
   "losses": {
-    "approx_kl": 0.0002155543354872082,
+    "approx_kl": 0.00022559609663273607,
     "clipfrac": 0.0,
-    "ema_max": 1.2262159883975983,
-    "entropy": 2.484058209827968,
-    "explained_variance": 0.16326427459716797,
-    "filter_threshold": 0.012262159883975983,
-    "filtered_fraction": 0.024684585847504104,
-    "kept_fraction": 0.9753154141524959,
-    "masked_fraction": 0.10986328125,
-    "old_approx_kl": -0.0011627360114029475,
-    "policy_loss": -0.0004513516489948545,
-    "value_loss": 0.08172488159367017
+    "ema_max": 1.2698969841003418,
+    "entropy": 2.4838708468845914,
+    "explained_variance": 0.07069230079650879,
+    "filter_threshold": 0.012698969841003419,
+    "filtered_fraction": 0.014412416851441234,
+    "kept_fraction": 0.9855875831485588,
+    "masked_fraction": 0.119140625,
+    "old_approx_kl": 0.0003389947648559298,
+    "policy_loss": -9.120744653046131e-05,
+    "value_loss": 0.0982145725616387
   },
   "meta": {
     "bptt_horizon": 64,

From a9ee68a186cfad94701a1bdc09676ac3fe607542 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 11:43:15 +0200
Subject: [PATCH 11/19] Add carla_lhs binaries for Town01 to Town10HD

---
 .gitignore                                    |   1 +
 .../binaries/carla_lhs/opendrive__Town01.bin  | Bin 0 -> 110684 bytes
 .../binaries/carla_lhs/opendrive__Town02.bin  | Bin 0 -> 58488 bytes
 .../binaries/carla_lhs/opendrive__Town03.bin  | Bin 0 -> 838364 bytes
 .../binaries/carla_lhs/opendrive__Town04.bin  | Bin 0 -> 1095140 bytes
 .../binaries/carla_lhs/opendrive__Town05.bin  | Bin 0 -> 1143904 bytes
 .../binaries/carla_lhs/opendrive__Town06.bin  | Bin 0 -> 1056872 bytes
 .../binaries/carla_lhs/opendrive__Town07.bin  | Bin 0 -> 402720 bytes
 .../carla_lhs/opendrive__Town10HD.bin         | Bin 0 -> 170556 bytes
 9 files changed, 1 insertion(+)
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town01.bin
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town02.bin
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town03.bin
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town04.bin
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town05.bin
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town06.bin
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town07.bin
 create mode 100644 pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town10HD.bin

diff --git a/.gitignore b/.gitignore
index be0cbb22bd..baa6caa7bf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -169,6 +169,7 @@ pufferlib/ocean/impulse_wars/benchmark/
 data/
 pufferlib/resources/drive/binaries/*/
 !pufferlib/resources/drive/binaries/carla/
+!pufferlib/resources/drive/binaries/carla_lhs/
 !pufferlib/resources/drive/binaries/carla/**
 # Re-ignore .DS_Store inside carla binaries
 pufferlib/resources/drive/binaries/carla/.DS_Store
diff --git a/pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town01.bin b/pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town01.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d7c5c68a29b5f1ce6bf2bb9009033312b2882657
GIT binary patch
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new file mode 100644
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diff --git a/pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town05.bin b/pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town05.bin
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diff --git a/pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town07.bin b/pufferlib/resources/drive/binaries/carla_lhs/opendrive__Town07.bin
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literal 0
HcmV?d00001


From c2326a0ba830a4bcb2895ce1bb61872941e44341 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 11:55:46 +0200
Subject: [PATCH 12/19] Update .dockerignore to include additional directories
 and file types

---
 .dockerignore | 39 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/.dockerignore b/.dockerignore
index 3a92c72017..a896e4ff2d 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -7,3 +7,42 @@ build
 *.egg-info
 experiments
 wandb
+.neptune
+.pytest_cache
+.ruff_cache
+
+benchmark*/
+!pufferlib/ocean/benchmark/
+!pufferlib/ocean/benchmark/**
+runs*/
+weights/
+checkpoints/
+/data/
+!/tests/smoke_tests/data/
+!/tests/smoke_tests/data/**
+/artifacts/
+external/
+
+pufferlib/resources/drive/binaries/*/
+!pufferlib/resources/drive/binaries/carla/
+!pufferlib/resources/drive/binaries/carla/**
+!pufferlib/resources/drive/binaries/dense/
+!pufferlib/resources/drive/binaries/dense/**
+!pufferlib/resources/drive/binaries/lateral/
+!pufferlib/resources/drive/binaries/lateral/**
+!pufferlib/resources/drive/binaries/longitudinal/
+!pufferlib/resources/drive/binaries/longitudinal/**
+!pufferlib/resources/drive/binaries/nuplan/
+!pufferlib/resources/drive/binaries/nuplan/**
+!pufferlib/resources/drive/binaries/obstacles/
+!pufferlib/resources/drive/binaries/obstacles/**
+!pufferlib/resources/drive/binaries/vru/
+!pufferlib/resources/drive/binaries/vru/**
+
+pufferlib/resources/drive/output*.gif
+pufferlib/resources/drive/pufferdrive_*.gif
+*.mp4
+*.mov
+*.webm
+*.avi
+*.mkv

From 2fdfcad4ff417619071923816c633e10607048e8 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 12:09:39 +0200
Subject: [PATCH 13/19] Refactor partner observation features to replace
 relative velocity with signed simulation speed

---
 notebooks/01_observations.py   |  3 +--
 notebooks/05_inference.py      | 20 ++++++++++----------
 pufferlib/ocean/drive/drive.h  |  8 +++-----
 pufferlib/ocean/drive/render.h |  6 +++---
 4 files changed, 17 insertions(+), 20 deletions(-)

diff --git a/notebooks/01_observations.py b/notebooks/01_observations.py
index 7a60c9825b..6677e62fb3 100644
--- a/notebooks/01_observations.py
+++ b/notebooks/01_observations.py
@@ -176,8 +176,7 @@
     "width",
     "heading_cos",
     "heading_sin",
-    "rel_vx",
-    "rel_vy",
+    "sim_speed_signed",
     "seconds_stopped",
 ]
 active_mask = ~np.all(partners == 0, axis=1)
diff --git a/notebooks/05_inference.py b/notebooks/05_inference.py
index eddcc1ad8e..26d6bc2795 100644
--- a/notebooks/05_inference.py
+++ b/notebooks/05_inference.py
@@ -259,7 +259,7 @@ def run_rollout(env, policy, deterministic=False, horizon=HORIZON):
 # - **Ego**: speed, width, length, [jerk: steering, a_long, a_lat], lane_center_dist, lane_angle, speed_limit
 # - **Conditioning** (if enabled): 17 reward coefs (goal_radius, goal_speed, collision, offroad, comfort, lane_align, vel_align, lane_center, center_bias, velocity, reverse, stop_line, timestep, overspeed, throttle, steer, acc) + target waypoints
 # - **Target**: static=rel_x,rel_y,rel_z per waypoint; dynamic=rel_x,rel_y,rel_z,heading_cos,heading_sin per waypoint
-# - **Partners** (MAX_PARTNERS x 9): rel_x, rel_y, rel_z, length, width, heading_cos, heading_sin, rel_vx, rel_vy, seconds_stopped
+# - **Partners** (MAX_PARTNERS x 9): rel_x, rel_y, rel_z, length, width, heading_cos, heading_sin, sim_speed_signed, seconds_stopped
 # - **Lanes** (MAX_LANES x 7): rel_x, rel_y, rel_z, seg_length, seg_width, dir_cos, dir_sin
 # - **Boundaries** (MAX_BOUNDS x 7): same as lanes
 # - **Traffic controls** (MAX_TRAFFIC x 7): rel_x1, rel_y1, rel_x2, rel_y2, rel_z, type, state
@@ -351,8 +351,7 @@ def layer_stats(name, arr):
     "width",
     "heading_cos",
     "heading_sin",
-    "rel_vx",
-    "rel_vy",
+    "sim_speed_signed",
     "seconds_stopped",
 ]
 for p in range(min(int(n_visible), 5)):
@@ -632,12 +631,14 @@ def unpack_all_timesteps(bufs, agent_idx):
 for i in range(partners.shape[0]):
     if np.allclose(partners[i], 0):
         continue
-    rx, ry, rz, w, l, hc, hs, vx, vy, _ = partners[i]
+    rx, ry, rz, length, width, hc, hs, speed, _ = partners[i]
     heading = np.arctan2(hs, hc)
-    rect = Rectangle((-l / 2, -w / 2), l, w, facecolor="orange", edgecolor="black", alpha=0.6, zorder=9)
+    rect = Rectangle(
+        (-length / 2, -width / 2), length, width, facecolor="orange", edgecolor="black", alpha=0.6, zorder=9
+    )
     rect.set_transform(plt.matplotlib.transforms.Affine2D().rotate(heading).translate(rx, ry) + ax.transData)
     ax.add_patch(rect)
-    ax.annotate(f"{vx:.2f}, {vy:.2f}", (rx, ry), fontsize=7, ha="center", color="darkred", zorder=12)
+    ax.annotate(f"{speed:.2f}", (rx, ry), fontsize=7, ha="center", color="darkred", zorder=12)
 part_mask = np.any(partners != 0, axis=1)
 if part_mask.any():
     ax.scatter(
@@ -774,8 +775,7 @@ def unpack_all_timesteps(bufs, agent_idx):
     "width",
     "heading_cos",
     "heading_sin",
-    "rel_vx",
-    "rel_vy",
+    "sim_speed_signed",
     "seconds_stopped",
 ]
 obs_slots_partners_n = env.obs_slots_partners_n
@@ -791,10 +791,10 @@ def unpack_all_timesteps(bufs, agent_idx):
 
 all_partners = buf_stoch["obs"][:, :, _p_start:_p_end].reshape(
     -1, obs_slots_partners_n, pf
-)  # (H*N, obs_slots_partners_n, 10)
+)  # (H*N, obs_slots_partners_n, pf)
 # Mask: partner is visible if any feature != 0
 visible_mask = np.any(all_partners != 0, axis=2)  # (H*N, 16)
-visible_partners = all_partners[visible_mask]  # (K, 10) — all visible partner observations
+visible_partners = all_partners[visible_mask]  # (K, pf) — all visible partner observations
 
 print(
     f"Total partner obs: {all_partners.shape[0] * obs_slots_partners_n}, visible: {len(visible_partners)} "
diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h
index 4a590bb18e..701eda9d53 100644
--- a/pufferlib/ocean/drive/drive.h
+++ b/pufferlib/ocean/drive/drive.h
@@ -126,7 +126,7 @@
 // Observation feature counts
 #define EGO_FEATURES 10
 #define ROAD_FEATURES 7
-#define PARTNER_FEATURES 10
+#define PARTNER_FEATURES 9
 #define TRAFFIC_CONTROL_FEATURES 7
 #define STATIC_TARGET_FEATURES 3
 #define DYNAMIC_TARGET_FEATURES 5
@@ -4702,10 +4702,9 @@ static int write_partner_obs(Drive *env, Agent *ego, int agent_idx, float *obs,
 
     for (int j = 0; j < partners_to_write; j++) {
         Agent *other = &env->agents[nearby_agents[j].index];
-        float rel_x, rel_y, rel_heading_x, rel_heading_y, rel_vx, rel_vy;
+        float rel_x, rel_y, rel_heading_x, rel_heading_y;
         project_vector_to_ego_frame(ego, nearby_agents[j].dx, nearby_agents[j].dy, &rel_x, &rel_y);
         project_vector_to_ego_frame(ego, other->cos_heading, other->sin_heading, &rel_heading_x, &rel_heading_y);
-        project_vector_to_ego_frame(ego, other->sim_vx - ego->sim_vx, other->sim_vy - ego->sim_vy, &rel_vx, &rel_vy);
         obs[obs_idx++] = rel_x / env->obs_norm_xy_offset_m;
         obs[obs_idx++] = rel_y / env->obs_norm_xy_offset_m;
         obs[obs_idx++] = nearby_agents[j].dz / Z_BUFFER;
@@ -4713,8 +4712,7 @@ static int write_partner_obs(Drive *env, Agent *ego, int agent_idx, float *obs,
         obs[obs_idx++] = other->sim_width / env->obs_norm_veh_width_m;
         obs[obs_idx++] = rel_heading_x;
         obs[obs_idx++] = rel_heading_y;
-        obs[obs_idx++] = rel_vx / (2.0f * MAX_SPEED);
-        obs[obs_idx++] = rel_vy / (2.0f * MAX_SPEED);
+        obs[obs_idx++] = other->sim_speed_signed / MAX_SPEED;
         // TODO(hack): partner seconds_stopped is a temporary feature; remove later.
         obs[obs_idx++] = fminf(1.0f, other->seconds_stopped / MAX_STOPPED_SECONDS);
         partners_written++;
diff --git a/pufferlib/ocean/drive/render.h b/pufferlib/ocean/drive/render.h
index 2cb1a54eca..440b8c3599 100644
--- a/pufferlib/ocean/drive/render.h
+++ b/pufferlib/ocean/drive/render.h
@@ -782,7 +782,7 @@ void draw_agent_obs(Drive *env, int agent_index, int mode, int obs_only, int las
         // Draw position of other agents
         float x = agent_obs[obs_idx] * env->obs_norm_xy_offset_m;
         float y = agent_obs[obs_idx + 1] * env->obs_norm_xy_offset_m;
-        float z = agent_obs[obs_idx + 2] * env->obs_norm_xy_offset_m;
+        float z = agent_obs[obs_idx + 2] * Z_BUFFER;
         if (lasers && mode == 0) {
             DrawLine3D((Vector3) {0, 0, 0}, (Vector3) {x, y, z + 1}, ORANGE);
         }
@@ -921,7 +921,7 @@ void draw_agent_obs(Drive *env, int agent_index, int mode, int obs_only, int las
         // For road segments, draw line between start and end points
         float x_middle = agent_obs[entity_idx] * env->obs_norm_xy_offset_m;
         float y_middle = agent_obs[entity_idx + 1] * env->obs_norm_xy_offset_m;
-        float z_middle = agent_obs[entity_idx + 2] * env->obs_norm_xy_offset_m;
+        float z_middle = agent_obs[entity_idx + 2] * Z_BUFFER;
         float rel_angle_x = (agent_obs[entity_idx + 5]);
         float rel_angle_y = (agent_obs[entity_idx + 6]);
         float rel_angle = atan2f(rel_angle_y, rel_angle_x);
@@ -975,7 +975,7 @@ void draw_agent_obs(Drive *env, int agent_index, int mode, int obs_only, int las
         }
         float x_middle = agent_obs[entity_idx] * env->obs_norm_xy_offset_m;
         float y_middle = agent_obs[entity_idx + 1] * env->obs_norm_xy_offset_m;
-        float z_middle = agent_obs[entity_idx + 2] * env->obs_norm_xy_offset_m;
+        float z_middle = agent_obs[entity_idx + 2] * Z_BUFFER;
         float rel_angle_x = agent_obs[entity_idx + 5];
         float rel_angle_y = agent_obs[entity_idx + 6];
         float rel_angle = atan2f(rel_angle_y, rel_angle_x);

From 3016104cff939c019e32c7b80be0158508814273 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 12:22:45 +0200
Subject: [PATCH 14/19] Update golden JSON files with revised environment
 metrics and loss values

---
 .../smoke_tests/data/drive_smoke_golden.json  | 60 +++++++++----------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/tests/smoke_tests/data/drive_smoke_golden.json b/tests/smoke_tests/data/drive_smoke_golden.json
index 01734a546f..027cd6619c 100644
--- a/tests/smoke_tests/data/drive_smoke_golden.json
+++ b/tests/smoke_tests/data/drive_smoke_golden.json
@@ -1,48 +1,48 @@
 {
   "env": {
-    "avg_distance_per_infraction": 14.85188889503479,
-    "avg_speed_per_agent": 1.4011926352977753,
-    "collision_rate": 0.0,
-    "comfort_violation_count": 0.7380953431129456,
+    "avg_distance_per_infraction": 12.82711124420166,
+    "avg_speed_per_agent": 1.3282413482666016,
+    "collision_rate": 0.025,
+    "comfort_violation_count": 0.7132158041000366,
     "dnf_rate": 0.5625,
-    "episode_length": 46.0,
-    "episode_return": -2.523432493209839,
-    "lane_center_rate": 0.705732524394989,
+    "episode_length": 41.8,
+    "episode_return": -2.303484392166138,
+    "lane_center_rate": 0.7117858052253723,
     "n": 16.0,
     "num_goals_reached": 0.0,
     "obs/max": 80.0,
-    "obs/mean": 0.22964997438248247,
-    "obs/min": -1.1160968635231256,
-    "offroad_rate": 0.40625,
-    "red_light_violation_rate": 0.03125,
+    "obs/mean": 0.22740022384095937,
+    "obs/min": -1.2169142998754978,
+    "offroad_rate": 0.3875,
+    "red_light_violation_rate": 0.025,
     "reward_components/ade": 0.0,
-    "reward_components/collision": 0.0,
-    "reward_components/comfort": -1.712499350309372,
+    "reward_components/collision": -0.04070625305175781,
+    "reward_components/comfort": -1.5124996423721313,
     "reward_components/goal": 0.0,
-    "reward_components/lane_align": -0.14824828505516052,
-    "reward_components/lane_center": -0.003005934282555245,
-    "reward_components/offroad": -0.609375,
+    "reward_components/lane_align": -0.12546229213476182,
+    "reward_components/lane_center": -0.0024075661320239307,
+    "reward_components/offroad": -0.58125,
     "reward_components/overspeed": 0.0,
-    "reward_components/red_light": -0.03125,
-    "reward_components/reverse": -0.018953118938952684,
-    "reward_components/timestep": -0.00010140621816390194,
+    "reward_components/red_light": -0.025,
+    "reward_components/reverse": -0.016068746522068977,
+    "reward_components/timestep": -9.056247654370964e-05,
     "reward_components/velocity": 0.0,
     "score": 0.0,
     "velocity_progress_sum": 0.0
   },
   "losses": {
-    "approx_kl": 0.00022559609663273607,
+    "approx_kl": 0.0001738829887472093,
     "clipfrac": 0.0,
-    "ema_max": 1.2698969841003418,
-    "entropy": 2.4838708468845914,
-    "explained_variance": 0.07069230079650879,
-    "filter_threshold": 0.012698969841003419,
-    "filtered_fraction": 0.014412416851441234,
-    "kept_fraction": 0.9855875831485588,
-    "masked_fraction": 0.119140625,
-    "old_approx_kl": 0.0003389947648559298,
-    "policy_loss": -9.120744653046131e-05,
-    "value_loss": 0.0982145725616387
+    "ema_max": 1.2449477910995483,
+    "entropy": 2.484184503555298,
+    "explained_variance": 0.219338059425354,
+    "filter_threshold": 0.012449477910995484,
+    "filtered_fraction": 0.0379676158570631,
+    "kept_fraction": 0.9620323841429369,
+    "masked_fraction": 0.12548828125,
+    "old_approx_kl": -0.000223852095327207,
+    "policy_loss": -0.0012485174063060964,
+    "value_loss": 0.08668117118733269
   },
   "meta": {
     "bptt_horizon": 64,

From 0161adb6f1f670a2d426e1ee810927724825c93c Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 13:10:12 +0200
Subject: [PATCH 15/19] Update golden JSON files and refactor input sizes in
 training configuration

---
 .../smoke_tests/data/drive_smoke_golden.json  | 62 +++++++++----------
 tests/smoke_tests/test_drive_train.py         | 18 +++---
 2 files changed, 40 insertions(+), 40 deletions(-)

diff --git a/tests/smoke_tests/data/drive_smoke_golden.json b/tests/smoke_tests/data/drive_smoke_golden.json
index 027cd6619c..e8b4778943 100644
--- a/tests/smoke_tests/data/drive_smoke_golden.json
+++ b/tests/smoke_tests/data/drive_smoke_golden.json
@@ -1,48 +1,48 @@
 {
   "env": {
-    "avg_distance_per_infraction": 12.82711124420166,
-    "avg_speed_per_agent": 1.3282413482666016,
-    "collision_rate": 0.025,
-    "comfort_violation_count": 0.7132158041000366,
+    "avg_distance_per_infraction": 15.107745885848999,
+    "avg_speed_per_agent": 1.4028075337409973,
+    "collision_rate": 0.0,
+    "comfort_violation_count": 0.7450608313083649,
     "dnf_rate": 0.5625,
-    "episode_length": 41.8,
-    "episode_return": -2.303484392166138,
-    "lane_center_rate": 0.7117858052253723,
+    "episode_length": 47.0,
+    "episode_return": -2.568606436252594,
+    "lane_center_rate": 0.7080668658018112,
     "n": 16.0,
     "num_goals_reached": 0.0,
     "obs/max": 80.0,
-    "obs/mean": 0.22740022384095937,
-    "obs/min": -1.2169142998754978,
-    "offroad_rate": 0.3875,
-    "red_light_violation_rate": 0.025,
+    "obs/mean": 0.23133239336311817,
+    "obs/min": -1.2116065127775073,
+    "offroad_rate": 0.40625,
+    "red_light_violation_rate": 0.03125,
     "reward_components/ade": 0.0,
-    "reward_components/collision": -0.04070625305175781,
-    "reward_components/comfort": -1.5124996423721313,
+    "reward_components/collision": 0.0,
+    "reward_components/comfort": -1.7539056837558746,
     "reward_components/goal": 0.0,
-    "reward_components/lane_align": -0.12546229213476182,
-    "reward_components/lane_center": -0.0024075661320239307,
-    "reward_components/offroad": -0.58125,
+    "reward_components/lane_align": -0.1518757250159979,
+    "reward_components/lane_center": -0.0031759651901666075,
+    "reward_components/offroad": -0.609375,
     "reward_components/overspeed": 0.0,
-    "reward_components/red_light": -0.025,
-    "reward_components/reverse": -0.016068746522068977,
-    "reward_components/timestep": -9.056247654370964e-05,
+    "reward_components/red_light": -0.03125,
+    "reward_components/reverse": -0.01892186817713082,
+    "reward_components/timestep": -0.0001030468392855255,
     "reward_components/velocity": 0.0,
     "score": 0.0,
     "velocity_progress_sum": 0.0
   },
   "losses": {
-    "approx_kl": 0.0001738829887472093,
-    "clipfrac": 0.0,
-    "ema_max": 1.2449477910995483,
-    "entropy": 2.484184503555298,
-    "explained_variance": 0.219338059425354,
-    "filter_threshold": 0.012449477910995484,
-    "filtered_fraction": 0.0379676158570631,
-    "kept_fraction": 0.9620323841429369,
-    "masked_fraction": 0.12548828125,
-    "old_approx_kl": -0.000223852095327207,
-    "policy_loss": -0.0012485174063060964,
-    "value_loss": 0.08668117118733269
+    "approx_kl": 0.008834710278149163,
+    "clipfrac": 0.12224070089203971,
+    "ema_max": 1.4194585680961609,
+    "entropy": 2.4655654770987376,
+    "explained_variance": 0.21746909618377686,
+    "filter_threshold": 0.01419458568096161,
+    "filtered_fraction": 0.02824551873981529,
+    "kept_fraction": 0.9717544812601847,
+    "masked_fraction": 0.10107421875,
+    "old_approx_kl": 0.00783040001988411,
+    "policy_loss": -0.00791241747460195,
+    "value_loss": 0.3491141710962568
   },
   "meta": {
     "bptt_horizon": 64,
diff --git a/tests/smoke_tests/test_drive_train.py b/tests/smoke_tests/test_drive_train.py
index a0aba76048..86942c1020 100644
--- a/tests/smoke_tests/test_drive_train.py
+++ b/tests/smoke_tests/test_drive_train.py
@@ -149,17 +149,17 @@ def _build_config():
         args["policy"],
         {
             "ego_input_size": 32,
-            "partner_input_size": 32,
-            "lane_input_size": 32,
-            "boundary_input_size": 32,
-            "traffic_control_input_size": 32,
-            "target_input_size": 32,
-            "backbone_hidden_size": 32,
-            "actor_hidden_size": 32,
-            "critic_hidden_size": 32,
+            "partner_input_size": 128,
+            "lane_input_size": 64,
+            "boundary_input_size": 64,
+            "traffic_control_input_size": 16,
+            "target_input_size": 8,
+            "backbone_hidden_size": 256,
+            "actor_hidden_size": 128,
+            "critic_hidden_size": 64,
         },
     )
-    _set_existing(args["rnn"], {"input_size": 32, "hidden_size": 32})
+    _set_existing(args["rnn"], {"input_size": 256, "hidden_size": 256})
 
     args["wandb"] = False
     args["neptune"] = False

From b80a6340bcf1ac2bfa801da4386f622205721d88 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 13:15:52 +0200
Subject: [PATCH 16/19] Add unit test for encoding and pooling of masked padded
 objects in DriveBackbone

---
 tests/unit_tests/test_drive_backbone.py | 48 +++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 tests/unit_tests/test_drive_backbone.py

diff --git a/tests/unit_tests/test_drive_backbone.py b/tests/unit_tests/test_drive_backbone.py
new file mode 100644
index 0000000000..a22031e84d
--- /dev/null
+++ b/tests/unit_tests/test_drive_backbone.py
@@ -0,0 +1,48 @@
+import torch
+
+from pufferlib.ocean.torch import DriveBackbone
+
+
+def test_encode_and_pool_masks_padded_objects():
+    backbone = object.__new__(DriveBackbone)
+    backbone.mask_padded_features = True
+
+    objects = torch.tensor(
+        [
+            [[1.0, 10.0], [2.0, 3.0], [100.0, 100.0], [200.0, 200.0]],
+            [[300.0, 300.0], [400.0, 400.0], [500.0, 500.0], [600.0, 600.0]],
+            [[4.0, 0.0], [-3.0, 7.0], [5.0, 1.0], [999.0, 999.0]],
+        ]
+    )
+    valid_counts = torch.tensor([2, 0, 3])
+    encoded_inputs = []
+
+    def encoder(x):
+        encoded_inputs.append(x)
+        return x
+
+    pooled = backbone._encode_and_pool(objects, valid_counts, encoder, 2)
+
+    assert len(encoded_inputs) == 1
+    torch.testing.assert_close(
+        encoded_inputs[0],
+        torch.tensor(
+            [
+                [1.0, 10.0],
+                [2.0, 3.0],
+                [4.0, 0.0],
+                [-3.0, 7.0],
+                [5.0, 1.0],
+            ]
+        ),
+    )
+    torch.testing.assert_close(
+        pooled,
+        torch.tensor(
+            [
+                [2.0, 10.0],
+                [0.0, 0.0],
+                [5.0, 7.0],
+            ]
+        ),
+    )

From 16667c9b5ec18781b1815b31d3e0e2f5c4ee2c96 Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 14:24:52 +0200
Subject: [PATCH 17/19] Renamed encoder to context

---
 notebooks/04_training.py              | 16 ++++-----
 notebooks/05_inference.py             |  6 ++--
 notebooks/06_architecture.py          | 52 +++++++++++++--------------
 notebooks/notebook_utils.py           |  2 +-
 pufferlib/config/ocean/drive.ini      |  2 +-
 pufferlib/ocean/drive/drive.py        |  4 +--
 pufferlib/ocean/torch.py              | 30 ++++++++--------
 tests/smoke_tests/test_drive_train.py |  2 +-
 8 files changed, 57 insertions(+), 57 deletions(-)

diff --git a/notebooks/04_training.py b/notebooks/04_training.py
index c1378bd2b1..b592cf963e 100644
--- a/notebooks/04_training.py
+++ b/notebooks/04_training.py
@@ -109,11 +109,11 @@
     f"ego_obs: shape={ego_obs.shape}, NaN={torch.isnan(ego_obs).sum().item()}, range=[{ego_obs.min():.3f}, {ego_obs.max():.3f}]"
 )
 
-cond_dim = backbone.target_dim
-if cond_dim > 0:
-    cond_obs = x[:, slide_idx : slide_idx + cond_dim]
-    slide_idx += cond_dim
-    print(f"cond_obs: shape={cond_obs.shape}, NaN={torch.isnan(cond_obs).sum().item()}")
+context_dim = backbone.context_dim
+if context_dim > 0:
+    context_obs = x[:, slide_idx : slide_idx + context_dim]
+    slide_idx += context_dim
+    print(f"context_obs: shape={context_obs.shape}, NaN={torch.isnan(context_obs).sum().item()}")
 
 partner_dim = env.obs_slots_partners_n * env.partner_features
 lane_dim = env.obs_slots_lane_kept * env.road_features
@@ -141,11 +141,11 @@
         f"{name:>10s}_enc: NaN={torch.isnan(enc).sum().item()}, dead={((enc.abs().sum(dim=0) == 0).sum().item())}, range=[{enc.min():.3f}, {enc.max():.3f}]"
     )
 
-if cond_dim > 0:
+if context_dim > 0:
     with torch.no_grad():
-        cond_enc = backbone.target_encoder(cond_obs)
+        context_enc = backbone.context_encoder(context_obs)
     print(
-        f"{'cond':>10s}_enc: NaN={torch.isnan(cond_enc).sum().item()}, dead={((cond_enc.abs().sum(dim=0) == 0).sum().item())}, range=[{cond_enc.min():.3f}, {cond_enc.max():.3f}]"
+        f"{'context':>10s}_enc: NaN={torch.isnan(context_enc).sum().item()}, dead={((context_enc.abs().sum(dim=0) == 0).sum().item())}, range=[{context_enc.min():.3f}, {context_enc.max():.3f}]"
     )
 
 # %% [markdown]
diff --git a/notebooks/05_inference.py b/notebooks/05_inference.py
index 26d6bc2795..761bcd7a6a 100644
--- a/notebooks/05_inference.py
+++ b/notebooks/05_inference.py
@@ -1349,8 +1349,8 @@ def compute_gae(rewards, values, terminals, truncations, gamma, lam):
             True,
         )
     )
-if bb.target_dim > 0:
-    enc_inventory.append(("conditioning", bb.target_encoder, bb.target_dim, 1, False))
+if bb.context_dim > 0:
+    enc_inventory.append(("context", bb.context_encoder, bb.context_dim, 1, False))
 
 enc_names = [n for n, *_ in enc_inventory]
 set_encs = [n for n, _, _, _, is_set in enc_inventory if is_set]
@@ -1389,7 +1389,7 @@ def fn(m, i, o):
 lane_dim = bb.obs_slots_lane_kept * bb.road_features_count
 boundary_dim = bb.obs_slots_boundary_kept * bb.road_features_count
 traffic_dim = bb.obs_slots_traffic_controls_n * bb.traffic_control_features_count
-_s = ego_dim + bb.target_dim
+_s = ego_dim + bb.context_dim
 sl = {}
 sl["partner"] = (_s, _s + partner_dim, bb.obs_slots_partners_n, bb.partner_features_count)
 _s += partner_dim
diff --git a/notebooks/06_architecture.py b/notebooks/06_architecture.py
index ddcd8eac41..879dc42f84 100644
--- a/notebooks/06_architecture.py
+++ b/notebooks/06_architecture.py
@@ -51,7 +51,7 @@
     lane_input_size=INPUT_SIZE,
     boundary_input_size=INPUT_SIZE,
     traffic_control_input_size=INPUT_SIZE,
-    target_input_size=INPUT_SIZE,
+    context_input_size=INPUT_SIZE,
     backbone_hidden_size=BACKBONE_HIDDEN_SIZE,
     backbone_num_layers=BACKBONE_NUM_LAYERS,
     actor_hidden_size=ACTOR_HIDDEN_SIZE,
@@ -87,12 +87,12 @@
 
 # %%
 backbone = policy.actor_backbone
-cond_dim = backbone.target_dim
+context_dim = backbone.context_dim
 
 # Collect encoder info
 encoders = [
     ("ego", env.ego_features, 1, "direct", INPUT_SIZE),
-    ("conditioning", cond_dim, 1, "direct", INPUT_SIZE) if cond_dim > 0 else None,
+    ("context", context_dim, 1, "direct", INPUT_SIZE) if context_dim > 0 else None,
     ("partner", env.partner_features, env.obs_slots_partners_n, "max-pool", INPUT_SIZE),
     ("lane", env.road_features, env.obs_slots_lane_kept, "max-pool", INPUT_SIZE),
     ("boundary", env.road_features, env.obs_slots_boundary_kept, "max-pool", INPUT_SIZE),
@@ -216,8 +216,8 @@ def count_params(module):
     "partner_encoder": backbone.partner_encoder,
     "traffic_ctrl_encoder": backbone.traffic_control_encoder,
 }
-if backbone.target_dim > 0:
-    components["target_encoder"] = backbone.target_encoder
+if backbone.context_dim > 0:
+    components["context_encoder"] = backbone.context_encoder
 components["backbone_mlp"] = backbone.backbone
 components["actor_head"] = policy.actor_head
 components["critic_head"] = policy.critic_head
@@ -254,7 +254,7 @@ def count_params(module):
 backbone = policy.actor_backbone
 
 slide_idx = env.ego_features
-cond_dim = backbone.target_dim
+context_dim = backbone.context_dim
 partner_dim = env.obs_slots_partners_n * env.partner_features
 lane_dim = env.obs_slots_lane_kept * env.road_features
 boundary_dim = env.obs_slots_boundary_kept * env.road_features
@@ -264,10 +264,10 @@ def count_params(module):
 ego_obs = x[:, :slide_idx]
 slices = [("ego", 0, slide_idx, ego_obs.shape)]
 
-if cond_dim > 0:
-    cond_obs = x[:, slide_idx : slide_idx + cond_dim]
-    slices.append(("conditioning", slide_idx, slide_idx + cond_dim, cond_obs.shape))
-    slide_idx += cond_dim
+if context_dim > 0:
+    context_obs = x[:, slide_idx : slide_idx + context_dim]
+    slices.append(("context", slide_idx, slide_idx + context_dim, context_obs.shape))
+    slide_idx += context_dim
 
 partner_obs = x[:, slide_idx : slide_idx + partner_dim]
 slices.append(("partners", slide_idx, slide_idx + partner_dim, partner_obs.shape))
@@ -296,9 +296,9 @@ def count_params(module):
     ego_enc = backbone.ego_encoder(ego_obs)
     print(f"  ego_encoder:     {ego_obs.shape} -> {ego_enc.shape}")
 
-    if cond_dim > 0:
-        cond_enc = backbone.target_encoder(cond_obs)
-        print(f"  cond_encoder:    {cond_obs.shape} -> {cond_enc.shape}")
+    if context_dim > 0:
+        context_enc = backbone.context_encoder(context_obs)
+        print(f"  context_encoder:    {context_obs.shape} -> {context_enc.shape}")
 
     p_reshaped = partner_obs.view(-1, env.obs_slots_partners_n, env.partner_features)
     p_enc, _ = backbone.partner_encoder(p_reshaped).max(dim=1)
@@ -326,8 +326,8 @@ def count_params(module):
 
     # Concat + backbone
     features = [ego_enc, l_enc, b_enc, p_enc, t_enc]
-    if cond_dim > 0:
-        features.append(cond_enc)
+    if context_dim > 0:
+        features.append(context_enc)
     concat = torch.cat(features, dim=1)
     hidden = backbone.backbone(concat)
     print(f"\n  concat: {concat.shape}")
@@ -382,9 +382,9 @@ def count_params(module):
     slide = env.ego_features
     activations["ego"] = backbone.ego_encoder(obs_tensor[:, : env.ego_features])
 
-    if cond_dim > 0:
-        activations["conditioning"] = backbone.target_encoder(obs_tensor[:, slide : slide + cond_dim])
-        slide += cond_dim
+    if context_dim > 0:
+        activations["context"] = backbone.context_encoder(obs_tensor[:, slide : slide + context_dim])
+        slide += context_dim
 
     p_obs = obs_tensor[:, slide : slide + partner_dim].view(-1, env.obs_slots_partners_n, env.partner_features)
     activations["partner"], _ = backbone.partner_encoder(p_obs).max(dim=1)
@@ -507,7 +507,7 @@ def count_params(module):
     "lane_input_size": 64,
     "boundary_input_size": 64,
     "traffic_control_input_size": 64,
-    "target_input_size": 64,
+    "context_input_size": 64,
     "backbone_num_layers": 1,
     "actor_hidden_size": 128,
     "actor_num_layers": 0,
@@ -533,7 +533,7 @@ def count_params(module):
             "lane_input_size": encoder_size,
             "boundary_input_size": encoder_size,
             "traffic_control_input_size": encoder_size,
-            "target_input_size": encoder_size,
+            "context_input_size": encoder_size,
         }
     )
     p = DrivePolicy(env, **full_cfg).to(device)
@@ -600,9 +600,9 @@ def count_params(module):
 
 slide = env.ego_features
 segments = [("ego", 0, env.ego_features, 1, env.ego_features)]
-if cond_dim > 0:
-    segments.append(("conditioning", slide, slide + cond_dim, 1, cond_dim))
-    slide += cond_dim
+if context_dim > 0:
+    segments.append(("context", slide, slide + context_dim, 1, context_dim))
+    slide += context_dim
 segments.append(("partners", slide, slide + partner_dim, env.obs_slots_partners_n, env.partner_features))
 slide += partner_dim
 segments.append(("lanes", slide, slide + lane_dim, env.obs_slots_lane_kept, env.road_features))
@@ -661,10 +661,10 @@ def count_params(module):
 seg_boundaries = [0, env.ego_features]
 seg_labels = ["ego"]
 s = env.ego_features
-if cond_dim > 0:
-    s += cond_dim
+if context_dim > 0:
+    s += context_dim
     seg_boundaries.append(s)
-    seg_labels.append("cond")
+    seg_labels.append("context")
 for name, dim in [
     ("partners", partner_dim),
     ("lanes", lane_dim),
diff --git a/notebooks/notebook_utils.py b/notebooks/notebook_utils.py
index 815cc58ee2..5afe94b2ee 100644
--- a/notebooks/notebook_utils.py
+++ b/notebooks/notebook_utils.py
@@ -97,7 +97,7 @@
     "lane_input_size": 64,
     "boundary_input_size": 64,
     "traffic_control_input_size": 64,
-    "target_input_size": 64,
+    "context_input_size": 64,
     "backbone_hidden_size": 128,
     "backbone_num_layers": 1,
     "actor_hidden_size": 128,
diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini
index b6b1efc9f8..cd6a7c4940 100644
--- a/pufferlib/config/ocean/drive.ini
+++ b/pufferlib/config/ocean/drive.ini
@@ -17,7 +17,7 @@ partner_input_size = 64
 lane_input_size = 64
 boundary_input_size = 64
 traffic_control_input_size = 64
-target_input_size = 64
+context_input_size = 64
 ; Encoder activation - options: "relu", "tanh", "gelu"
 encoder_activation = "relu"
 encoder_layer_norm = True
diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py
index f7a017ead2..03abef5363 100644
--- a/pufferlib/ocean/drive/drive.py
+++ b/pufferlib/ocean/drive/drive.py
@@ -223,12 +223,12 @@ def __init__(
             self.target_features = binding.STATIC_TARGET_FEATURES
         else:
             self.target_features = binding.DYNAMIC_TARGET_FEATURES
-        self.goal_dim = self.num_target_waypoints * self.target_features
+        self.target_dim = self.num_target_waypoints * self.target_features
 
         self.num_obs = (
             self.ego_features
             + self.num_reward_coefs
-            + self.goal_dim
+            + self.target_dim
             + self.obs_slots_partners_n * self.partner_features
             + self.obs_slots_lane_kept * self.road_features
             + self.obs_slots_boundary_kept * self.road_features
diff --git a/pufferlib/ocean/torch.py b/pufferlib/ocean/torch.py
index 3e33d32420..ae50921e8c 100644
--- a/pufferlib/ocean/torch.py
+++ b/pufferlib/ocean/torch.py
@@ -50,7 +50,7 @@ def __init__(
         lane_input_size,
         boundary_input_size,
         traffic_control_input_size,
-        target_input_size,
+        context_input_size,
         backbone_hidden_size,
         backbone_num_layers,
         ego_dim,
@@ -69,7 +69,7 @@ def __init__(
         self.lane_input_size = lane_input_size
         self.boundary_input_size = boundary_input_size
         self.traffic_control_input_size = traffic_control_input_size
-        self.target_input_size = target_input_size
+        self.context_input_size = context_input_size
 
         # Observation dimensions from environment config
         self.obs_slots_partners_n = env.obs_slots_partners_n
@@ -89,8 +89,8 @@ def __init__(
         )
         self.obs_slot_num_types = binding.OBS_SLOT_NUM_TYPES
         self.mask_padded_features = mask_padded_features
-        # Conditioning size (reward coefficients + target info)
-        self.target_dim = env.num_reward_coefs + env.goal_dim
+        # Context size (reward coefficients + target info)
+        self.context_dim = env.num_reward_coefs + env.target_dim
 
         # 1. observations Encoders
         # Each encoder projects raw features into its own embedding space
@@ -110,9 +110,9 @@ def __init__(
                 self.traffic_control_features_after_onehot, traffic_control_input_size
             )
             encoders_out += traffic_control_input_size
-        if self.target_dim > 0:
-            self.target_encoder = self._create_encoder(self.target_dim, target_input_size)
-            encoders_out += target_input_size
+        if self.context_dim > 0:
+            self.context_encoder = self._create_encoder(self.context_dim, context_input_size)
+            encoders_out += context_input_size
 
         # 2. Main Backbone MLP
         backbone_act_cls = ACTIVATIONS[backbone_activation]
@@ -139,8 +139,8 @@ def forward(self, observations, ego_dim):
         slide_idx = ego_dim
         ego_observations = observations[:, :slide_idx]
 
-        target_observations = observations[:, slide_idx : slide_idx + self.target_dim]
-        slide_idx += self.target_dim
+        context_observations = observations[:, slide_idx : slide_idx + self.context_dim]
+        slide_idx += self.context_dim
 
         partner_observations = observations[:, slide_idx : slide_idx + partner_dim]
         slide_idx += partner_dim
@@ -227,9 +227,9 @@ def forward(self, observations, ego_dim):
             feature_list.append(traffic_control_features)
 
         # Add optional features if enabled
-        if self.target_dim > 0:
-            target_features = self.target_encoder(target_observations)
-            feature_list.append(target_features)
+        if self.context_dim > 0:
+            context_features = self.context_encoder(context_observations)
+            feature_list.append(context_features)
 
         # Concatenate all features and pass through main backbone
         concat_features = torch.cat(feature_list, dim=1)
@@ -241,7 +241,7 @@ def pool_slot_counts(self, observations, ego_dim):
         boundary_dim = self.obs_slots_boundary_kept * self.road_features_count
         traffic_control_dim = self.obs_slots_traffic_controls_n * self.traffic_control_features_count
 
-        slide_idx = ego_dim + self.target_dim
+        slide_idx = ego_dim + self.context_dim
         partner_observations = observations[:, slide_idx : slide_idx + partner_dim]
         slide_idx += partner_dim
         lane_observations = observations[:, slide_idx : slide_idx + lane_dim]
@@ -312,7 +312,7 @@ def __init__(
         lane_input_size: int,
         boundary_input_size: int,
         traffic_control_input_size: int,
-        target_input_size: int,
+        context_input_size: int,
         backbone_hidden_size: int,
         backbone_num_layers: int,
         actor_hidden_size: int,
@@ -340,7 +340,7 @@ def __init__(
             "lane_input_size": lane_input_size,
             "boundary_input_size": boundary_input_size,
             "traffic_control_input_size": traffic_control_input_size,
-            "target_input_size": target_input_size,
+            "context_input_size": context_input_size,
             "backbone_hidden_size": backbone_hidden_size,
             "backbone_num_layers": backbone_num_layers,
             "ego_dim": self.ego_dim,
diff --git a/tests/smoke_tests/test_drive_train.py b/tests/smoke_tests/test_drive_train.py
index 86942c1020..cd394871b0 100644
--- a/tests/smoke_tests/test_drive_train.py
+++ b/tests/smoke_tests/test_drive_train.py
@@ -153,7 +153,7 @@ def _build_config():
             "lane_input_size": 64,
             "boundary_input_size": 64,
             "traffic_control_input_size": 16,
-            "target_input_size": 8,
+            "context_input_size": 8,
             "backbone_hidden_size": 256,
             "actor_hidden_size": 128,
             "critic_hidden_size": 64,

From c94955ea4cbf8064190073d1d15a9b71b8adcf5a Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 14:42:20 +0200
Subject: [PATCH 18/19] Rename 'conditioning' to 'context' in inference
 processing

---
 notebooks/05_inference.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/05_inference.py b/notebooks/05_inference.py
index 761bcd7a6a..8ab2896493 100644
--- a/notebooks/05_inference.py
+++ b/notebooks/05_inference.py
@@ -1422,7 +1422,7 @@ def fn(m, i, o):
     winners[name] = masked.max(dim=1).indices  # (B, embedding dim): winning slot per dim
     pooled[name] = torch.where(vm.unsqueeze(1), masked.max(dim=1).values, torch.zeros_like(masked.max(dim=1).values))
 
-for name in ("ego", "conditioning"):
+for name in ("ego", "context"):
     if name in enc_names:
         pooled[name] = captured[name]
 

From 2069382bf81aed3fc6b7cc7b6a2e187c1813601f Mon Sep 17 00:00:00 2001
From: Valentin Charraut <valentin.charraut@valeo.com>
Date: Fri, 5 Jun 2026 15:54:00 +0200
Subject: [PATCH 19/19] Rename OBS_SLOT_NUM_TYPES to OBS_VALID_COUNT_FEATURES
 across multiple files

---
 notebooks/01_observations.py   | 2 +-
 pufferlib/ocean/drive/drive.h  | 4 ++--
 pufferlib/ocean/drive/drive.py | 4 ++--
 pufferlib/ocean/env_binding.h  | 2 +-
 pufferlib/ocean/torch.py       | 4 ++--
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/notebooks/01_observations.py b/notebooks/01_observations.py
index 6677e62fb3..99608478e1 100644
--- a/notebooks/01_observations.py
+++ b/notebooks/01_observations.py
@@ -138,7 +138,7 @@
 idx += env.obs_slots_traffic_controls_n * env.traffic_control_features
 assert np.allclose(traffic_manual, traffic), "traffic mismatch"
 
-idx += 4  # appended slot-count features at end of obs
+idx += env.obs_valid_count_features
 assert idx == obs.shape[1], f"obs size mismatch: used {idx}, total {obs.shape[1]}"
 print(f"All slices match. Total features used: {idx}")
 
diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h
index 9ef76fab7a..183f55017e 100644
--- a/pufferlib/ocean/drive/drive.h
+++ b/pufferlib/ocean/drive/drive.h
@@ -130,7 +130,7 @@
 #define TRAFFIC_CONTROL_FEATURES 7
 #define STATIC_TARGET_FEATURES 3
 #define DYNAMIC_TARGET_FEATURES 5
-#define OBS_SLOT_NUM_TYPES 4
+#define OBS_VALID_COUNT_FEATURES 4
 
 // GIGAFLOW specific
 #define MAX_ROUTE_LENGTH 64
@@ -3897,7 +3897,7 @@ static int compute_observation_size(Drive *env) {
                                                               : 0;
     return EGO_FEATURES + PARTNER_FEATURES * env->obs_slots_partners_n
         + ROAD_FEATURES * (env->obs_slots_lane_kept + env->obs_slots_boundary_kept)
-        + TRAFFIC_CONTROL_FEATURES * env->obs_slots_traffic_controls_n + OBS_SLOT_NUM_TYPES
+        + TRAFFIC_CONTROL_FEATURES * env->obs_slots_traffic_controls_n + OBS_VALID_COUNT_FEATURES
         + env->reward_conditioning * NUM_REWARD_COEFS + env->num_target_waypoints * target_features;
 }
 
diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py
index 03abef5363..cc05e7004e 100644
--- a/pufferlib/ocean/drive/drive.py
+++ b/pufferlib/ocean/drive/drive.py
@@ -215,7 +215,7 @@ def __init__(
         self.partner_features = binding.PARTNER_FEATURES
         self.road_features = binding.ROAD_FEATURES
         self.traffic_control_features = binding.TRAFFIC_CONTROL_FEATURES
-        self.obs_slot_num_types = binding.OBS_SLOT_NUM_TYPES
+        self.obs_valid_count_features = binding.OBS_VALID_COUNT_FEATURES
         self.num_reward_coefs = binding.NUM_REWARD_COEFS if reward_conditioning else 0
 
         # Target features based on target_type
@@ -233,7 +233,7 @@ def __init__(
             + self.obs_slots_lane_kept * self.road_features
             + self.obs_slots_boundary_kept * self.road_features
             + self.obs_slots_traffic_controls_n * self.traffic_control_features
-            + self.obs_slot_num_types
+            + self.obs_valid_count_features
         )
 
         self.single_observation_space = gymnasium.spaces.Box(low=-1, high=1, shape=(self.num_obs,), dtype=np.float32)
diff --git a/pufferlib/ocean/env_binding.h b/pufferlib/ocean/env_binding.h
index 860d37c662..4953a3f64f 100644
--- a/pufferlib/ocean/env_binding.h
+++ b/pufferlib/ocean/env_binding.h
@@ -1380,7 +1380,7 @@ PyMODINIT_FUNC PyInit_binding(void) {
     PyModule_AddIntConstant(m, "ROAD_FEATURES", ROAD_FEATURES);
     PyModule_AddIntConstant(m, "PARTNER_FEATURES", PARTNER_FEATURES);
     PyModule_AddIntConstant(m, "TRAFFIC_CONTROL_FEATURES", TRAFFIC_CONTROL_FEATURES);
-    PyModule_AddIntConstant(m, "OBS_SLOT_NUM_TYPES", OBS_SLOT_NUM_TYPES);
+    PyModule_AddIntConstant(m, "OBS_VALID_COUNT_FEATURES", OBS_VALID_COUNT_FEATURES);
     PyModule_AddIntConstant(m, "NUM_TRAFFIC_CONTROL_TYPES", NUM_TRAFFIC_CONTROL_TYPES);
     PyModule_AddIntConstant(m, "NUM_TRAFFIC_CONTROL_STATES", NUM_TRAFFIC_CONTROL_STATES);
     PyModule_AddIntConstant(m, "TRAFFIC_CONTROL_TYPE_NONE", TRAFFIC_CONTROL_TYPE_NONE);
diff --git a/pufferlib/ocean/torch.py b/pufferlib/ocean/torch.py
index ae50921e8c..6bcf1533c5 100644
--- a/pufferlib/ocean/torch.py
+++ b/pufferlib/ocean/torch.py
@@ -87,7 +87,7 @@ def __init__(
             + binding.NUM_TRAFFIC_CONTROL_TYPES
             + binding.NUM_TRAFFIC_CONTROL_STATES
         )
-        self.obs_slot_num_types = binding.OBS_SLOT_NUM_TYPES
+        self.obs_valid_count_features = binding.OBS_VALID_COUNT_FEATURES
         self.mask_padded_features = mask_padded_features
         # Context size (reward coefficients + target info)
         self.context_dim = env.num_reward_coefs + env.target_dim
@@ -153,7 +153,7 @@ def forward(self, observations, ego_dim):
 
         traffic_control_observations = observations[:, slide_idx : slide_idx + traffic_control_dim]
         count_observations = observations[
-            :, slide_idx + traffic_control_dim : slide_idx + traffic_control_dim + self.obs_slot_num_types
+            :, slide_idx + traffic_control_dim : slide_idx + traffic_control_dim + self.obs_valid_count_features
         ]
         lane_counts, boundary_counts, partner_counts, traffic_control_counts = [
             count_observations[:, i].long().clamp_(0, capacity)