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40 行
1.3 KiB
40 行
1.3 KiB
from mlagents_envs.gym_to_unity_wrapper import GymToUnityWrapper
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from mlagents_envs.base_env import ActionType
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import gym
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import pytest
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GYM_ENVS = ["CartPole-v1", "MountainCar-v0"]
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@pytest.mark.parametrize("name", GYM_ENVS, ids=GYM_ENVS)
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def test_creation(name):
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env = GymToUnityWrapper(gym.make(name), name)
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env.close()
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@pytest.mark.parametrize("name", GYM_ENVS, ids=GYM_ENVS)
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def test_specs(name):
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gym_env = gym.make(name)
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env = GymToUnityWrapper(gym_env, name)
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assert env.get_behavior_names()[0] == name
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if isinstance(gym_env.action_space, gym.spaces.Box):
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assert env.get_behavior_spec(name).action_type == ActionType.CONTINUOUS
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elif isinstance(gym_env.action_space, gym.spaces.Discrete):
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assert env.get_behavior_spec(name).action_type == ActionType.DISCRETE
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else:
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raise NotImplementedError("Test for this action space type not implemented")
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env.close()
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@pytest.mark.parametrize("name", GYM_ENVS, ids=GYM_ENVS)
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def test_steps(name):
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env = GymToUnityWrapper(gym.make(name), name)
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spec = env.get_behavior_spec(name)
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env.reset()
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for _ in range(200):
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d_steps, t_steps = env.get_steps(name)
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env.set_actions(name, spec.create_empty_action(len(d_steps)))
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env.step()
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env.close()
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