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import pytest |
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import numpy as np |
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from easydict import EasyDict |
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from zoo.box2d.lunarlander.envs import LunarLanderEnv |
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@pytest.mark.envtest |
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@pytest.mark.parametrize( |
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'cfg', [ |
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EasyDict({ |
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'env_name': 'LunarLander-v2', |
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'act_scale': False, |
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'replay_path': None, |
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'replay_path_gif': None, |
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'save_replay_gif': False, |
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}), |
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EasyDict({ |
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'env_name': 'LunarLanderContinuous-v2', |
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'act_scale': True, |
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'replay_path': None, |
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'replay_path_gif': None, |
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'save_replay_gif': False, |
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}) |
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] |
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) |
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class TestLunarLanderEnvEnv: |
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""" |
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Overview: |
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The env created for testing the LunarLander environment. |
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It is used to check information such as observation space, action space and reward space. |
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""" |
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def test_naive(self, cfg): |
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env = LunarLanderEnv(cfg) |
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env.seed(314) |
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assert env._seed == 314 |
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obs = env.reset() |
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assert obs['observation'].shape == (8, ) |
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for i in range(10): |
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random_action = env.random_action() |
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timestep = env.step(random_action) |
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print(timestep) |
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assert isinstance(timestep.obs['observation'], np.ndarray) |
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assert isinstance(timestep.done, bool) |
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assert timestep.obs['observation'].shape == (8, ) |
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assert timestep.reward.shape == (1, ) |
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assert timestep.reward >= env.reward_space.low |
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assert timestep.reward <= env.reward_space.high |
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print(env.observation_space, env.action_space, env.reward_space) |
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env.close() |
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