Machine Learning Trainer

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

ml/run_eval.py

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+"""Run a trained policy in the PyBullet sim for quick inspection.
+
+Usage:
+    python ml/run_eval.py --model ml/checkpoints/ppo_joint_command.zip --episodes 3 --gui
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+ROOT_DIR = Path(__file__).resolve().parent.parent
+if str(ROOT_DIR) not in sys.path:
+    sys.path.insert(0, str(ROOT_DIR))
+
+from ml.evaluate import evaluate
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", type=str, required=True, help="Path to the trained model file (.zip)")
+    parser.add_argument("--episodes", type=int, default=3, help="Number of 'rounds' to run. One episode lasts from reset until the robot falls over or the time limit is reached.")
+    parser.add_argument("--gui", action="store_true", help="Show the PyBullet GUI during evaluation")
+    parser.add_argument("--num-robots", type=int, default=1, help="Number of robots in the evaluation 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(
+        model_path=args.model,
+        episodes=args.episodes,
+        use_gui=args.gui,
+        num_robots=args.num_robots,
+        robot_spacing=args.robot_spacing,
+        start_pose=args.start_pose,
+    )
+
+
+if __name__ == "__main__":
+    main()