mirror of
https://github.com/velocitatem/PHANTOM.git
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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import wandb
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from stable_baselines3 import SAC
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from stable_baselines3.common.callbacks import EvalCallback
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from .wrapper import PHANTOM
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from .lib import EconomicMetricsWrapper, MetricsCallback
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wandb.init(
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project="phantom-pricing",
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config={"alpha": 0.3, "n_products": 10, "total_timesteps": 50000}
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)
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env = EconomicMetricsWrapper(PHANTOM(n_products=10, alpha=0.3, render_mode=None))
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eval_env = EconomicMetricsWrapper(PHANTOM(n_products=10, alpha=0.3, render_mode=None))
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model = SAC(
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"MultiInputPolicy",
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env,
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verbose=1,
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learning_rate=3e-4,
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buffer_size=50000,
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batch_size=256,
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tau=0.005,
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gamma=0.99,
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)
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metrics_cb = MetricsCallback(log_histograms=True, log_freq=100)
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eval_cb = EvalCallback(eval_env, eval_freq=1000, n_eval_episodes=5, verbose=1)
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model.learn(total_timesteps=50000, callback=[metrics_cb, eval_cb])
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model.save("phantom_sac")
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wandb.finish()
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# test trained policy
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env = PHANTOM(n_products=10, alpha=0.3, render_mode="human")
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obs, _ = env.reset()
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for _ in range(100):
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action, _ = model.predict(obs, deterministic=True)
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obs, reward, term, trunc, _ = env.step(action)
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env.render()
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if term or trunc: break
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env.close()
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