Updates SB3 ppo cfg so it trains under reasonable amount of time (#3726)

# Description

This PR fixes the sb3_ppo_cfg for task Isaac-Ant-v0

the parameter before had 4096 num_envs + horizon 512 + batch size 128 +
n_epoch 20,
that means the training one cycle it needs to for loop (20 * 512 * 4096)
/ 128 = 327680 times!

which appears as if it is hanging forever

the new config matches more closely with that of rl_games.

I verified it will trains under 5 min 

[Screencast from 2025-10-15
13-56-21.webm](https://github.com/user-attachments/assets/2bc7bcd8-0063-46b9-adb0-67a6aa686732)

## Type of change

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- Bug fix (non-breaking change which fixes an issue)

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## Checklist

- [x] I have read and understood the [contribution
guidelines](https://isaac-sim.github.io/IsaacLab/main/source/refs/contributing.html)
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- [ ] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
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Author: ooctipus

source/isaaclab_rl/config/extension.toml

@@ -1,7 +1,7 @@
 [package]
 
 # Note: Semantic Versioning is used: https://semver.org/
-version = "0.4.1"
+version = "0.4.2"
 
 # Description
 title = "Isaac Lab RL"

source/isaaclab_rl/docs/CHANGELOG.rst

@@ -1,6 +1,15 @@
 Changelog
 ---------
 
+0.4.2 (2025-10-15)
+~~~~~~~~~~~~~~~~~~
+
+Fixed
+^^^^^
+
+* Isaac-Ant-v0's sb3_ppo_cfg default value, so it trains under reasonable amount of time.
+
+
 0.4.1 (2025-09-09)
 ~~~~~~~~~~~~~~~~~~
 

source/isaaclab_tasks/isaaclab_tasks/manager_based/classic/ant/agents/sb3_ppo_cfg.yaml

@@ -6,19 +6,19 @@
 # Reference: https://github.com/DLR-RM/rl-baselines3-zoo/blob/master/hyperparams/ppo.yml#L161
 seed: 42
 
-n_timesteps: !!float 1e7
+n_timesteps: !!float 1e8
 policy: 'MlpPolicy'
-batch_size: 128
-n_steps: 512
+batch_size: 32768
+n_steps: 16
 gamma: 0.99
 gae_lambda: 0.9
-n_epochs: 20
+n_epochs: 4
 ent_coef: 0.0
 sde_sample_freq: 4
 max_grad_norm: 0.5
 vf_coef: 0.5
 learning_rate: !!float 3e-5
-use_sde: True
+use_sde: False
 clip_range: 0.4
 device: "cuda:0"
 policy_kwargs: