mirror of
https://github.com/saymrwulf/stable-baselines3.git
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489b1fdaf2
* Fixed noise to return float32 * Updated changelog * Fixed test to use numpy arrays instead of python floats * Sorted imports for tests * Added dtype to constructor * Removed dtype parameter for VectorizedActionNoise * __init__ -> None; Capitalize and period in docstring when needed; fix dtype type hint; dtype in docstring * fix dtype type hint * Update version * Clarify changelog [skip ci] * empty commit to run ci * Update docs/misc/changelog.rst --------- Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com> Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
import numpy as np
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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-v1"
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if algo in [TD3, SAC]:
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kwargs.update(
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{"action_noise": NormalActionNoise(np.zeros(1), 0.1 * np.ones(1)), "learning_starts": 100, "train_freq": 4}
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)
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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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