mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-06-26 03:01:19 +00:00
84 lines
2.5 KiB
Python
84 lines
2.5 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, 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(
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"MlpPolicy",
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"Pendulum-v0",
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policy_kwargs=dict(net_arch=[64, 64]),
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learning_starts=100,
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verbose=1,
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create_eval_env=True,
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action_noise=action_noise,
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)
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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(
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"MlpPolicy",
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env_id,
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seed=0,
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policy_kwargs=dict(net_arch=[16]),
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verbose=1,
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create_eval_env=True,
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clip_range_vf=clip_range_vf,
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)
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else:
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model = PPO(
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"MlpPolicy",
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env_id,
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seed=0,
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policy_kwargs=dict(net_arch=[16]),
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verbose=1,
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create_eval_env=True,
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clip_range_vf=clip_range_vf,
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)
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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(
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"MlpPolicy",
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"Pendulum-v0",
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policy_kwargs=dict(net_arch=[64, 64]),
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learning_starts=100,
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verbose=1,
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create_eval_env=True,
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ent_coef=ent_coef,
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action_noise=NormalActionNoise(np.zeros(1), np.zeros(1)),
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)
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model.learn(total_timesteps=1000, eval_freq=500)
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def test_dqn():
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model = DQN(
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"MlpPolicy",
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"CartPole-v1",
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policy_kwargs=dict(net_arch=[64, 64]),
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learning_starts=500,
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buffer_size=500,
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learning_rate=3e-4,
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verbose=1,
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create_eval_env=True,
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)
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model.learn(total_timesteps=1000, eval_freq=500)
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