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| from easydict import EasyDict | |
| import pytest | |
| import gymnasium as gym | |
| import numpy as np | |
| from ding.envs import DingEnvWrapper | |
| class TestDingEnvWrapper: | |
| def test(self): | |
| env_id = 'Pendulum-v1' | |
| env = gym.make(env_id) | |
| ding_env = DingEnvWrapper(env=env) | |
| print(ding_env.observation_space, ding_env.action_space, ding_env.reward_space) | |
| cfg = EasyDict(dict( | |
| collector_env_num=16, | |
| evaluator_env_num=3, | |
| is_train=True, | |
| )) | |
| l1 = ding_env.create_collector_env_cfg(cfg) | |
| assert isinstance(l1, list) | |
| l1 = ding_env.create_evaluator_env_cfg(cfg) | |
| assert isinstance(l1, list) | |
| obs = ding_env.reset() | |
| assert isinstance(obs[0], np.ndarray) | |
| action = ding_env.random_action() | |
| print('random_action: {}, action_space: {}'.format(action.shape, ding_env.action_space)) | |