mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-18 21:30:19 +00:00
e9a8979022
* Add copy and combine method to running mean std * Update test * Faster test * Update test * Update test * Shift values in RMS test
37 lines
1.3 KiB
Python
37 lines
1.3 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 = 500
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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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env_id = "Pendulum-v0"
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if algo in [TD3, SAC]:
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kwargs.update({"action_noise": NormalActionNoise(0.0, 0.1), "learning_starts": 100, "train_freq": 4})
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else:
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if algo == DQN:
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env_id = "CartPole-v1"
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kwargs.update({"learning_starts": 100, "target_update_interval": 100})
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elif algo == PPO:
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kwargs.update({"n_steps": 64, "n_epochs": 4})
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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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