Machine Learning Trainer

Training environment to make a walk model for the hexapod
generated code that will be checked

ml/evaluate.py

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+import argparse
+import numpy as np
+
+from .env import JackBotEnv
+
+
+def evaluate(
+    model_path: str,
+    episodes: int = 5,
+    use_gui: bool = False,
+    num_robots: int = 1,
+    robot_spacing: float = 0.5,
+    start_pose: str = "init_deg",
+):
+    try:
+        from stable_baselines3 import PPO
+    except ImportError as exc:
+        raise ImportError(
+            "stable-baselines3 is required for evaluation. Install with: pip install stable-baselines3"
+        ) from exc
+
+    env = JackBotEnv(
+        use_gui=use_gui,
+        random_command=False,
+        num_robots=num_robots,
+        robot_spacing=robot_spacing,
+        start_pose=start_pose,
+    )
+    model = PPO.load(model_path)
+
+    for episode in range(episodes):
+        reset_res = env.reset()
+        # handle Gym / Gymnasium compatibility: reset may return (obs, info)
+        if isinstance(reset_res, tuple) and len(reset_res) == 2:
+            obs, _ = reset_res
+        else:
+            obs = reset_res
+
+        done = False
+        episode_reward = 0.0
+
+        while not done:
+            # pass only the observation to the policy
+            action, _ = model.predict(obs, deterministic=True)
+
+            step_res = env.step(action)
+            # Gymnasium-style: (obs, reward, terminated, truncated, info)
+            if isinstance(step_res, tuple) and len(step_res) == 5:
+                obs, reward, terminated, truncated, info = step_res
+                done = bool(terminated or truncated)
+            else:
+                # legacy Gym: (obs, reward, done, info)
+                obs, reward, done, info = step_res
+
+            episode_reward += float(reward)
+
+        print(f"Episode {episode + 1}: reward={episode_reward:.2f}")
+
+    env.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Evaluate a trained JackBot policy.")
+    parser.add_argument("--model-path", type=str, required=True)
+    parser.add_argument("--episodes", type=int, default=5)
+    parser.add_argument("--gui", action="store_true")
+    parser.add_argument("--num-robots", type=int, default=1, help="Number of robots in the environment")
+    parser.add_argument("--robot-spacing", type=float, default=0.5, help="Spacing between robots in meters")
+    parser.add_argument("--start-pose", type=str, choices=["init_deg", "init90_deg"], default="init_deg", help="Initial robot pose at reset")
+    args = parser.parse_args()
+    evaluate(
+        args.model_path,
+        episodes=args.episodes,
+        use_gui=args.gui,
+        num_robots=args.num_robots,
+        robot_spacing=args.robot_spacing,
+        start_pose=args.start_pose,
+    )