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35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
import pytest
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from stable_baselines3 import A2C, DQN, 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, DQN, 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), "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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