Machine Learning Trainer

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

ml/run_train.py

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+"""Minimal training launcher for quick experiments.
+
+Usage:
+    python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command
+
+This is a convenience wrapper around `ml.train.train` with friendly defaults
+for interactive experimentation.
+"""
+
+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.train import train
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--timesteps", type=int, default=50000, help="Total number of 'practice steps'.")
+    parser.add_argument("--model", type=str, default="ml/checkpoints/ppo_joint_command", help="Path to save the trained model")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed for reproducibility")
+    parser.add_argument("--device", type=str, default="auto", help="Device to use: 'cpu', 'cuda', or 'auto'")
+    parser.add_argument("--gui", action="store_true", help="Show the PyBullet GUI during training")
+    parser.add_argument("--num-robots", type=int, default=1, help="Number of robots in the training 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()
+
+    Path(args.model).parent.mkdir(parents=True, exist_ok=True)
+    train(
+        total_timesteps=args.timesteps,
+        model_path=args.model,
+        seed=args.seed,
+        device=args.device,
+        use_gui=args.gui,
+        num_robots=args.num_robots,
+        robot_spacing=args.robot_spacing,
+        start_pose=args.start_pose,
+    )
+
+
+if __name__ == "__main__":
+    main()