File size: 2,236 Bytes
c1a4584 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | #!/usr/bin/env python3
"""Train V2D-G1-TableObject-v0 with the abstract PPO trainer.
Must be launched through Isaac Lab (AppLauncher) so Sim starts first:
cd simulation && source .venv/bin/activate
../rl/train.sh --headless --num_envs 64
"""
from __future__ import annotations
import argparse
import os
import sys
from datetime import datetime
from pathlib import Path
from isaaclab.app import AppLauncher
parser = argparse.ArgumentParser(description="Train G1 table-object RL (algorithm stub).")
parser.add_argument("--task", type=str, default="V2D-G1-TableObject-v0")
parser.add_argument("--num_envs", type=int, default=None)
parser.add_argument("--max_iterations", type=int, default=None)
parser.add_argument("--seed", type=int, default=42)
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
import gymnasium as gym # noqa: E402
from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper # noqa: E402
from isaaclab_tasks.utils import parse_env_cfg # noqa: E402
import v2d_sim # noqa: E402, F401
from v2d_rl.algorithms.ppo import RslRlPpoTrainer # noqa: E402
def main() -> None:
env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs)
env = gym.make(args_cli.task, cfg=env_cfg)
spec = gym.spec(args_cli.task)
agent_cfg_entry = spec.kwargs["rsl_rl_cfg_entry_point"]
mod_name, cls_name = agent_cfg_entry.rsplit(":", 1)
import importlib
agent_cfg = getattr(importlib.import_module(mod_name), cls_name)()
if args_cli.max_iterations is not None:
agent_cfg.max_iterations = args_cli.max_iterations
agent_cfg.seed = args_cli.seed
env = RslRlVecEnvWrapper(env)
log_root = Path(os.environ.get("RUNS_DIR", "runs")) / "rl" / args_cli.task
log_dir = str(log_root / datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
Path(log_dir).mkdir(parents=True, exist_ok=True)
print(f"[v2d-rl] task={args_cli.task} log={log_dir}", flush=True)
trainer = RslRlPpoTrainer(agent_cfg)
trainer.train(env, log_dir=log_dir)
env.close()
if __name__ == "__main__":
try:
main()
finally:
simulation_app.close()
sys.exit(0)
|