Datasets:
File size: 2,675 Bytes
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 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | 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 stable_baselines3 import DQN, PPO, SAC
from solarchain_eval.config import load_config
from solarchain_eval.evaluate import SB3Policy, evaluate_policies
from solarchain_eval.policies import make_builtin_policy
from solarchain_eval.run_metadata import write_run_metadata
from solarchain_eval.train import train_model
def main() -> None:
parser = argparse.ArgumentParser(description="Run all SolarChain-Eval baselines")
parser.add_argument("--config", default="configs/default.yaml")
parser.add_argument("--timesteps", type=int, default=2048)
parser.add_argument("--episodes", type=int, default=2)
parser.add_argument("--output-dir", default=None)
parser.add_argument("--run-name", default=None)
parser.add_argument("--skip-rl", action="store_true")
parser.add_argument("--no-physics-penalty", action="store_true")
parser.add_argument("--no-progress", action="store_true")
args = parser.parse_args()
config = load_config(args.config)
if args.no_physics_penalty:
config.no_physics_penalty = True
run_id = args.run_name or datetime.now().strftime("%Y%m%d_%H%M%S")
run_dir = Path(args.output_dir or config.output_dir) / run_id
model_paths = {}
if not args.skip_rl:
for algo in ["ppo", "sac", "dqn"]:
model_paths[algo] = train_model(algo, config, args.timesteps, run_dir / "models" / algo)
policies = [
make_builtin_policy("static", config),
make_builtin_policy("random", config),
make_builtin_policy("myopic", config),
]
if not args.skip_rl:
config.action_mode = "continuous"
policies.append(SB3Policy(PPO.load(model_paths["ppo"]), "ppo"))
policies.append(SB3Policy(SAC.load(model_paths["sac"]), "sac"))
config.action_mode = "discrete"
policies.append(SB3Policy(DQN.load(model_paths["dqn"]), "dqn"))
config.action_mode = "continuous"
evaluate_policies(policies, config, args.episodes, run_dir, show_progress=not args.no_progress)
write_run_metadata(
run_dir,
run_type="run_all_baselines",
args={**vars(args), "run_id": run_id, "run_dir": str(run_dir)},
config=config,
extra={
"model_paths": {key: str(value) for key, value in model_paths.items()},
"policies": [getattr(policy, "name", "policy") for policy in policies],
},
)
print(f"Wrote benchmark run to {run_dir}")
if __name__ == "__main__":
main()
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