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70 行
2.9 KiB
70 行
2.9 KiB
import unittest.mock as mock
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import pytest
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import numpy as np
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from gym import spaces
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from gym_unity.envs import UnityEnv, UnityGymException
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from mock_communicator import MockCommunicator
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@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
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@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
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def test_gym_wrapper(mock_communicator, mock_launcher):
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mock_communicator.return_value = MockCommunicator(
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discrete_action=False, visual_inputs=0, stack=False, num_agents=1)
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# Test for incorrect number of agents.
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with pytest.raises(UnityGymException):
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UnityEnv(' ', use_visual=False, multiagent=True)
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env = UnityEnv(' ', use_visual=False)
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assert isinstance(env, UnityEnv)
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assert isinstance(env.reset(), np.ndarray)
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actions = env.action_space.sample()
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assert actions.shape[0] == 2
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obs, rew, done, info = env.step(actions)
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assert isinstance(obs, np.ndarray)
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assert isinstance(rew, float)
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assert isinstance(done, bool)
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assert isinstance(info, dict)
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@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
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@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
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def test_multi_agent(mock_communicator, mock_launcher):
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mock_communicator.return_value = MockCommunicator(
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discrete_action=False, visual_inputs=0, stack=False, num_agents=2)
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# Test for incorrect number of agents.
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with pytest.raises(UnityGymException):
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UnityEnv(' ', multiagent=False)
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env = UnityEnv(' ', use_visual=False, multiagent=True)
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assert isinstance(env.reset(), list)
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actions = [env.action_space.sample() for i in range(env.number_agents)]
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obs, rew, done, info = env.step(actions)
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assert isinstance(obs, list)
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assert isinstance(rew, list)
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assert isinstance(done, list)
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assert isinstance(info, dict)
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@mock.patch('gym_unity.envs.unity_env.UnityEnvironment')
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def test_branched_flatten(mock_env):
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mock_env.return_value.academy_name = 'MockAcademy'
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mock_brain = mock.Mock();
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mock_brain.return_value.number_visual_observations = 0
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mock_brain.return_value.num_stacked_vector_observations = 1
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mock_brain.return_value.vector_action_space_type = 'discrete'
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mock_brain.return_value.vector_observation_space_size = 1
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# Unflattened action space
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mock_brain.return_value.vector_action_space_size = [2,2,3]
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mock_env.return_value.brains = {'MockBrain':mock_brain()}
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mock_env.return_value.external_brain_names = ['MockBrain']
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env = UnityEnv(' ', use_visual=False, multiagent=False, flatten_branched=True)
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assert isinstance(env.action_space, spaces.Discrete)
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assert env.action_space.n==12
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assert env._flattener.lookup_action(0)==[0,0,0]
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assert env._flattener.lookup_action(11)==[1,1,2]
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# Check that False produces a MultiDiscrete
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env = UnityEnv(' ', use_visual=False, multiagent=False, flatten_branched=False)
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assert isinstance(env.action_space, spaces.MultiDiscrete)
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