JackM323
61cb0150f0
new Metrics and SimManager for live training viewing Robot.py usage added to machinelearning
113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
import argparse
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import os
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from pathlib import Path
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from .env import JackBotEnv
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def parse_args():
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parser = argparse.ArgumentParser(description="Train a joint-command policy for JackBot.")
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parser.add_argument("--timesteps", type=int, default=500_000, help="Total training timesteps")
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parser.add_argument("--model-path", type=str, default="ml/checkpoints/ppo_joint_command", help="Where to save the trained model")
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument("--device", type=str, default="auto", help="Device to use: 'cpu', 'cuda', or 'auto' to autodetect")
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parser.add_argument("--use-gui", action="store_true", help="Enable PyBullet GUI during training")
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parser.add_argument("--num-robots", type=int, default=1, help="Number of robots in the training environment")
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parser.add_argument("--robot-spacing", type=float, default=0.5, 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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return parser.parse_args()
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def train(
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total_timesteps: int,
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model_path: str,
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seed: int = 0,
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device: str = "auto",
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use_gui: bool = False,
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num_robots: int = 1,
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robot_spacing: float = 0.5,
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start_pose: str = "init_deg",
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):
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try:
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import DummyVecEnv
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except ImportError as exc:
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raise ImportError(
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"stable-baselines3 and gym are required for training. "
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"Install them with: pip install stable-baselines3 gym"
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) from exc
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env = DummyVecEnv([
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lambda: JackBotEnv(
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use_gui=use_gui,
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random_command=True,
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num_robots=num_robots,
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robot_spacing=robot_spacing,
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start_pose=start_pose,
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)
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])
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def resolve_device(requested_device: str) -> str:
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try:
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import torch
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except ImportError:
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if requested_device != "cpu":
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raise RuntimeError(
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"PyTorch is not installed in the active environment. "
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"Install torch with a GPU-enabled build before using --device cuda."
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)
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return "cpu"
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hip_supported = getattr(torch.version, "hip", None) is not None
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cuda_available = torch.cuda.is_available()
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hip_available = hip_supported and getattr(torch.backends, "hip", None) is not None and torch.backends.hip.is_available()
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if requested_device == "auto":
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if hip_available or cuda_available:
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return "cuda"
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return "cpu"
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if requested_device in {"cuda", "gpu", "hip"}:
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if hip_available or cuda_available:
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return "cuda"
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raise RuntimeError(
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f"GPU device requested ({requested_device}) but no CUDA/ROCm-capable PyTorch is available. "
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f"Installed torch build: {torch.__version__} (hip={getattr(torch.version, 'hip', None)}, cuda={cuda_available})"
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)
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if requested_device == "cpu":
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return "cpu"
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raise ValueError(
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f"Unsupported device '{requested_device}'. Use 'cpu', 'cuda', or 'auto'."
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)
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device = resolve_device(device)
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model = PPO(
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"MlpPolicy",
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env,
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verbose=1,
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seed=seed,
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device=device,
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tensorboard_log=str(Path(__file__).resolve().parent / "tensorboard"),
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)
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model.learn(total_timesteps=total_timesteps)
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Path(model_path).parent.mkdir(parents=True, exist_ok=True)
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model.save(model_path)
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env.close()
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if __name__ == "__main__":
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args = parse_args()
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train(
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args.timesteps,
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args.model_path,
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seed=args.seed,
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device=args.device,
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use_gui=args.use_gui,
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num_robots=args.num_robots,
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robot_spacing=args.robot_spacing,
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start_pose=args.start_pose,
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) |