mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-26 22:45:15 +00:00
b833207142
* Change saving/loading normalization parameters to use single pickle file * Remove 'use_gae' from RolloutBuffer compute_returns function * Add some missing tests for normalizer, nan-checker and PPO clip_value_fn argument * Update changelog * Fix typo * Use proper pytest.raises for catching errors in tests * Add comment on GAE and how to obtain non-GAE behaviour * Remove save/load_running_average from VecNormalize in favor of load/save * Update changelog * Update docstring * Add accidentally removed tests for VecNormalize Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
import numpy as np
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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, OrnsteinUhlenbeckActionNoise
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normal_action_noise = NormalActionNoise(np.zeros(1), 0.1 * np.ones(1))
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@pytest.mark.parametrize('action_noise', [normal_action_noise, OrnsteinUhlenbeckActionNoise(np.zeros(1), 0.1 * np.ones(1))])
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def test_td3(action_noise):
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model = TD3('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[64, 64]),
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learning_starts=100, verbose=1, create_eval_env=True, action_noise=action_noise)
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model.learn(total_timesteps=1000, eval_freq=500)
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@pytest.mark.parametrize("env_id", ['CartPole-v1', 'Pendulum-v0'])
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def test_a2c(env_id):
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model = A2C('MlpPolicy', env_id, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True)
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model.learn(total_timesteps=1000, eval_freq=500)
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@pytest.mark.parametrize("env_id", ['CartPole-v1', 'Pendulum-v0'])
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@pytest.mark.parametrize("clip_range_vf", [None, 0.2, -0.2])
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def test_ppo(env_id, clip_range_vf):
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if clip_range_vf is not None and clip_range_vf < 0:
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# Should throw an error
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with pytest.raises(AssertionError):
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model = PPO('MlpPolicy', env_id, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True,
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clip_range_vf=clip_range_vf)
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else:
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model = PPO('MlpPolicy', env_id, seed=0, policy_kwargs=dict(net_arch=[16]), verbose=1, create_eval_env=True,
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clip_range_vf=clip_range_vf)
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model.learn(total_timesteps=1000, eval_freq=500)
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@pytest.mark.parametrize("ent_coef", ['auto', 0.01, 'auto_0.01'])
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def test_sac(ent_coef):
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model = SAC('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[64, 64]),
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learning_starts=100, verbose=1, create_eval_env=True, ent_coef=ent_coef,
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action_noise=NormalActionNoise(np.zeros(1), np.zeros(1)))
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model.learn(total_timesteps=1000, eval_freq=500)
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