mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-16 21:10:08 +00:00
35 lines
1.6 KiB
Python
35 lines
1.6 KiB
Python
import numpy as np
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import pytest
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from torchy_baselines import A2C, CEMRL, PPO, SAC, TD3
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from torchy_baselines.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
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action_noise = NormalActionNoise(np.zeros(1), 0.1 * np.ones(1))
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@pytest.mark.parametrize('action_noise', [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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def test_cemrl():
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model = CEMRL('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[16]), pop_size=2, n_grad=1,
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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("model_class", [A2C, PPO])
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@pytest.mark.parametrize("env_id", ['CartPole-v1', 'Pendulum-v0'])
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def test_onpolicy(model_class, env_id):
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model = model_class('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("ent_coef", ['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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