Datasets:
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4bd5225 | 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 | from __future__ import annotations
import argparse
from datetime import datetime
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from solarchain_eval.config import load_config
from solarchain_eval.run_metadata import write_run_metadata
from solarchain_eval.train import train_model
def main() -> None:
parser = argparse.ArgumentParser(description="Train SolarChain-Eval RL baselines")
parser.add_argument("--config", default="configs/default.yaml")
parser.add_argument("--algo", choices=["ppo", "sac", "dqn"], default="ppo")
parser.add_argument("--timesteps", type=int, default=None)
parser.add_argument("--output-dir", default=None)
parser.add_argument("--run-name", default=None)
parser.add_argument("--no-timestamp", action="store_true")
parser.add_argument("--no-physics-penalty", action="store_true")
args = parser.parse_args()
config = load_config(args.config)
if args.no_physics_penalty:
config.no_physics_penalty = True
timesteps = args.timesteps or config.training.timesteps
base_output = Path(args.output_dir or config.output_dir)
run_name = args.run_name or f"{args.algo}_train"
if args.no_timestamp:
output_dir = base_output / run_name
else:
stamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_dir = base_output / f"{stamp}_{run_name}"
model_path = train_model(args.algo, config, timesteps, output_dir)
write_run_metadata(
output_dir,
run_type="train",
args={**vars(args), "resolved_timesteps": timesteps, "output_dir": str(output_dir)},
config=config,
extra={"model_path": str(model_path)},
)
print(f"Saved {args.algo.upper()} model to {model_path}")
print(f"Run output directory: {output_dir}")
if __name__ == "__main__":
main()
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