JackM323
acb3d671be
new reward/penalty system learning phases with curriculum learning new training parameters cleanup of old code better logging while training multiple environments instead of robots (they could bumb into each other)
48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
"""Minimal training launcher for quick experiments.
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Usage:
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python ml/run_train.py --timesteps 500000 --model ml/checkpoints/ppo_joint_command
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This is a convenience wrapper around `ml.train.train` with friendly defaults
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for interactive experimentation.
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"""
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import argparse
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import sys
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from pathlib import Path
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ROOT_DIR = Path(__file__).resolve().parent.parent
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if str(ROOT_DIR) not in sys.path:
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sys.path.insert(0, str(ROOT_DIR))
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from ml.train import train
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--timesteps", type=int, default=500000, help="Total number of 'practice steps'.")
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parser.add_argument("--model", type=str, default="ml/checkpoints/ppo_joint_command", help="Path to save the trained model")
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parser.add_argument("--seed", type=int, default=0, help="Random seed for reproducibility")
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parser.add_argument("--device", type=str, default="auto", help="Device to use: 'cpu', 'cuda', or 'auto'")
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parser.add_argument("--gui", action="store_true", help="Show the PyBullet GUI during training")
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parser.add_argument("--num-workers", type=int, default=1, help="Number of training environment workers")
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parser.add_argument("--robot-spacing", type=float, default=1.5, help="Spacing between robots in meters")
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parser.add_argument("--start-pose", type=str, choices=["init_deg", "init90_deg"], default="init_deg", help="Initial robot pose at reset")
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args = parser.parse_args()
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Path(args.model).parent.mkdir(parents=True, exist_ok=True)
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train(
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total_timesteps=args.timesteps,
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model_path=args.model,
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seed=args.seed,
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device=args.device,
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use_gui=args.gui,
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num_workers=args.num_workers,
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robot_spacing=args.robot_spacing,
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start_pose=args.start_pose,
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)
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if __name__ == "__main__":
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main()
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