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c6a97e17ee
* Initial version of vectorized environments * Raise an exception in the main process if child process raises an exception * Add list of exposed functions in vector module * Use deepcopy instead of np.copy * Add documentation for vector utils * Add tests for copy in AsyncVectorEnv * Add example in documentation for batch_space * Add cloudpickle dependency in setup.py * Fix __del__ in VectorEnv * Check if all observation spaces are equal in AsyncVectorEnv * Check if all observation spaces are equal in SyncVectorEnv * Fix spaces non equality in SyncVectorEnv for Python 2 * Handle None parameter in create_empty_array * Fix check_observation_space with spaces equality * Raise an exception when operations are out of order in AsyncVectorEnv * Add version requirement for cloudpickle * Use a state instead of binary flags in AsyncVectorEnv * Use numpy.zeros when initializing observations in vectorized environments * Remove poll from public API in AsyncVectorEnv * Remove close_extras from VectorEnv * Add test between AsyncVectorEnv and SyncVectorEnv * Remove close in check_observation_space * Add documentation for seed and close * Refactor exceptions for AsyncVectorEnv * Close pipes if the environment raises an error * Add tests for out of order operations * Change default argument in create_empty_array to np.zeros * Add get_attr and set_attr methods to VectorEnv * Improve consistency in SyncVectorEnv
40 lines
2.0 KiB
Python
40 lines
2.0 KiB
Python
import pytest
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import numpy as np
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from gym.spaces import Box, MultiDiscrete, Tuple, Dict
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from gym.vector.tests.utils import spaces
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from gym.vector.utils.spaces import _BaseGymSpaces, batch_space
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expected_batch_spaces_4 = [
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Box(low=-1., high=1., shape=(4,), dtype=np.float64),
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Box(low=0., high=10., shape=(4, 1), dtype=np.float32),
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Box(low=np.array([[-1., 0., 0.], [-1., 0., 0.], [-1., 0., 0.], [-1., 0., 0.]]),
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high=np.array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]), dtype=np.float32),
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Box(low=np.array([[[-1., 0.], [0., -1.]], [[-1., 0.], [0., -1.]], [[-1., 0.], [0., -1]],
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[[-1., 0.], [0., -1.]]]), high=np.ones((4, 2, 2)), dtype=np.float32),
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Box(low=0, high=255, shape=(4,), dtype=np.uint8),
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Box(low=0, high=255, shape=(4, 32, 32, 3), dtype=np.uint8),
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MultiDiscrete([2, 2, 2, 2]),
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Tuple((MultiDiscrete([3, 3, 3, 3]), MultiDiscrete([5, 5, 5, 5]))),
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Tuple((MultiDiscrete([7, 7, 7, 7]), Box(low=np.array([[0., -1.], [0., -1.], [0., -1.], [0., -1]]),
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high=np.array([[1., 1.], [1., 1.], [1., 1.], [1., 1.]]), dtype=np.float32))),
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Box(low=np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]),
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high=np.array([[10, 12, 16], [10, 12, 16], [10, 12, 16], [10, 12, 16]]), dtype=np.int64),
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Box(low=0, high=1, shape=(4, 19), dtype=np.int8),
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Dict({
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'position': MultiDiscrete([23, 23, 23, 23]),
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'velocity': Box(low=0., high=1., shape=(4, 1), dtype=np.float32)
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}),
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Dict({
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'position': Dict({'x': MultiDiscrete([29, 29, 29, 29]), 'y': MultiDiscrete([31, 31, 31, 31])}),
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'velocity': Tuple((MultiDiscrete([37, 37, 37, 37]), Box(low=0, high=255, shape=(4,), dtype=np.uint8)))
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})
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]
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@pytest.mark.parametrize('space,expected_batch_space_4', list(zip(spaces,
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expected_batch_spaces_4)), ids=[space.__class__.__name__ for space in spaces])
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def test_batch_space(space, expected_batch_space_4):
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batch_space_4 = batch_space(space, n=4)
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assert batch_space_4 == expected_batch_space_4
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