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https://github.com/saymrwulf/stable-baselines3.git
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36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
import pytest
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from stable_baselines3 import A2C, PPO, SAC, TD3
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from stable_baselines3.common.noise import NormalActionNoise
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N_STEPS_TRAINING = 3000
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SEED = 0
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@pytest.mark.parametrize("algo", [A2C, PPO, SAC, TD3])
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def test_deterministic_training_common(algo):
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results = [[], []]
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rewards = [[], []]
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# Smaller network
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kwargs = {'policy_kwargs': dict(net_arch=[64])}
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if algo in [TD3, SAC]:
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env_id = 'Pendulum-v0'
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kwargs.update({'action_noise': NormalActionNoise(0.0, 0.1),
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'learning_starts': 100})
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else:
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env_id = 'CartPole-v1'
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# if algo == DQN:
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# kwargs.update({'learning_starts': 100})
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for i in range(2):
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model = algo('MlpPolicy', env_id, seed=SEED, **kwargs)
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model.learn(N_STEPS_TRAINING)
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env = model.get_env()
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obs = env.reset()
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for _ in range(100):
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action, _ = model.predict(obs, deterministic=False)
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obs, reward, _, _ = env.step(action)
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results[i].append(action)
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rewards[i].append(reward)
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assert sum(results[0]) == sum(results[1]), results
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assert sum(rewards[0]) == sum(rewards[1]), rewards
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