diff --git a/scripts/reinforcement_learning/ray/tuner.py b/scripts/reinforcement_learning/ray/tuner.py
index c9d5d6e20b9..63c72184063 100644
--- a/scripts/reinforcement_learning/ray/tuner.py
+++ b/scripts/reinforcement_learning/ray/tuner.py
@@ -217,17 +217,17 @@ def invoke_tuning_run(cfg: dict, args: argparse.Namespace) -> None:
     print("[WARNING]: Not saving checkpoints, just running experiment...")
     print("[INFO]: Model parameters and metrics will be preserved.")
     print("[WARNING]: For homogeneous cluster resources only...")
+
+    # Initialize Ray
+    util.ray_init(
+        ray_address=args.ray_address,
+        log_to_driver=True,
+    )
+
     # Get available resources
     resources = util.get_gpu_node_resources()
     print(f"[INFO]: Available resources {resources}")
 
-    if not ray.is_initialized():
-        ray.init(
-            address=args.ray_address,
-            log_to_driver=True,
-            num_gpus=len(resources),
-        )
-
     print(f"[INFO]: Using config {cfg}")
 
     # Configure the search algorithm and the repeater
diff --git a/scripts/reinforcement_learning/ray/util.py b/scripts/reinforcement_learning/ray/util.py
index 427c887cdcc..26a52a90aba 100644
--- a/scripts/reinforcement_learning/ray/util.py
+++ b/scripts/reinforcement_learning/ray/util.py
@@ -320,6 +320,8 @@ def ray_init(ray_address: str = "auto", runtime_env: dict[str, Any] | None = Non
             f" runtime_env={runtime_env}"
         )
         ray.init(address=ray_address, runtime_env=runtime_env, log_to_driver=log_to_driver)
+    else:
+        print("[WARNING]: Attempting to initialize Ray but it is already initialized!")
 
 
 def get_gpu_node_resources(
@@ -343,7 +345,7 @@ def get_gpu_node_resources(
         or simply the resource for a single node if requested.
     """
     if not ray.is_initialized():
-        ray_init()
+        raise RuntimeError("Ray must be initialized before calling get_gpu_node_resources().")
     nodes = ray.nodes()
     node_resources = []
     total_cpus = 0