mirror of
https://github.com/saymrwulf/stable-baselines3.git
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43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
import gym
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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.vec_env import DummyVecEnv
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MODEL_LIST = [
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CEMRL,
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PPO,
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A2C,
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TD3,
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SAC,
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]
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@pytest.mark.parametrize("model_class", MODEL_LIST)
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def test_auto_wrap(model_class):
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# test auto wrapping of env into a VecEnv
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env = gym.make('Pendulum-v0')
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eval_env = gym.make('Pendulum-v0')
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model = model_class('MlpPolicy', env)
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model.learn(100, eval_env=eval_env)
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@pytest.mark.parametrize("model_class", MODEL_LIST)
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@pytest.mark.parametrize("env_id", ['Pendulum-v0', 'CartPole-v1'])
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def test_predict(model_class, env_id):
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if env_id == 'CartPole-v1' and model_class not in [PPO, A2C]:
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return
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# test detection of different shapes by the predict method
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model = model_class('MlpPolicy', env_id)
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env = gym.make(env_id)
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vec_env = DummyVecEnv([lambda: gym.make(env_id), lambda: gym.make(env_id)])
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obs = env.reset()
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action = model.predict(obs)
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assert action.shape == env.action_space.shape
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assert env.action_space.contains(action)
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vec_env_obs = vec_env.reset()
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action = model.predict(vec_env_obs)
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assert action.shape[0] == vec_env_obs.shape[0]
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