Datasets:
File size: 2,147 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 | from __future__ import annotations
from copy import deepcopy
from pathlib import Path
from stable_baselines3 import DQN, PPO, SAC
from stable_baselines3.common.monitor import Monitor
from .config import BenchmarkConfig
from .data import load_benchmark_data
from .env import SolarChainBenchmarkEnv
def make_env(config: BenchmarkConfig):
data = load_benchmark_data(config.data_dir)
def _factory():
return Monitor(SolarChainBenchmarkEnv(config=config, data=data))
return _factory
def train_model(algo: str, config: BenchmarkConfig, timesteps: int, output_dir: str | Path):
normalized = algo.lower().strip()
local_config = deepcopy(config)
local_config.action_mode = "discrete" if normalized == "dqn" else "continuous"
env = make_env(local_config)()
output = Path(output_dir)
output.mkdir(parents=True, exist_ok=True)
if normalized == "ppo":
model = PPO(
"MlpPolicy",
env,
seed=local_config.seed,
learning_rate=local_config.training.learning_rate,
batch_size=local_config.training.batch_size,
gamma=local_config.training.gamma,
verbose=1,
)
elif normalized == "sac":
model = SAC(
"MlpPolicy",
env,
seed=local_config.seed,
learning_rate=local_config.training.learning_rate,
batch_size=local_config.training.batch_size,
gamma=local_config.training.gamma,
verbose=1,
)
elif normalized == "dqn":
model = DQN(
"MlpPolicy",
env,
seed=local_config.seed,
learning_rate=local_config.training.learning_rate,
batch_size=local_config.training.batch_size,
gamma=local_config.training.gamma,
learning_starts=min(100, max(1, timesteps // 10)),
verbose=1,
)
else:
raise ValueError(f"Unsupported algorithm: {algo}")
model.learn(total_timesteps=timesteps)
model_path = output / f"{normalized}_model"
model.save(model_path)
return model_path.with_suffix(".zip")
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