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cd812b0d25
* rename Space.val_matches_space_definition -> contains; Space.is_point_in_space_shape -> valid_index * rename valid_index -> is_valid_index
199 lines
6.7 KiB
Python
199 lines
6.7 KiB
Python
import os
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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import pytest
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from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace, MultiSelectActionSpace, ObservationSpace, AgentSelection, VectorObservationSpace, AttentionActionSpace
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import numpy as np
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@pytest.mark.unit_test
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def test_discrete():
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action_space = DiscreteActionSpace(3, ["zero", "one", "two"])
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assert action_space.shape == 1
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for i in range(100):
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assert 3 > action_space.sample() >= 0
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action_info = action_space.sample_with_info()
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assert action_info.action_probability == 1. / 3
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assert action_space.high == 2
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assert action_space.low == 0
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# list descriptions
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assert action_space.get_description(1) == "one"
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# dict descriptions
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action_space = DiscreteActionSpace(3, {1: "one", 2: "two", 0: "zero"})
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assert action_space.get_description(0) == "zero"
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# no descriptions
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action_space = DiscreteActionSpace(3)
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assert action_space.get_description(0) == "0"
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# descriptions for invalid action
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with pytest.raises(ValueError):
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assert action_space.get_description(3) == "0"
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@pytest.mark.unit_test
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def test_box():
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# simple action space
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action_space = BoxActionSpace(4, -5, 5, ["a", "b", "c", "d"])
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for i in range(100):
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sample = action_space.sample()
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assert np.all(-5 <= sample) and np.all(sample <= 5)
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assert sample.shape == (4,)
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assert sample.dtype == float
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# test clipping
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clipped_action = action_space.clip_action_to_space(np.array([-10, 10, 2, 5]))
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assert np.all(clipped_action == np.array([-5, 5, 2, 5]))
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# more complex high and low definition
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action_space = BoxActionSpace(4, np.array([-5, -1, -0.5, 0]), np.array([1, 2, 4, 5]), ["a", "b", "c", "d"])
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for i in range(100):
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sample = action_space.sample()
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assert np.all(np.array([-5, -1, -0.5, 0]) <= sample) and np.all(sample <= np.array([1, 2, 4, 5]))
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assert sample.shape == (4,)
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assert sample.dtype == float
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# test clipping
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clipped_action = action_space.clip_action_to_space(np.array([-10, 10, 2, 5]))
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assert np.all(clipped_action == np.array([-5, 2, 2, 5]))
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# mixed high and low definition
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action_space = BoxActionSpace(4, np.array([-5, -1, -0.5, 0]), 5, ["a", "b", "c", "d"])
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for i in range(100):
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sample = action_space.sample()
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assert np.all(np.array([-5, -1, -0.5, 0]) <= sample) and np.all(sample <= 5)
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assert sample.shape == (4,)
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assert sample.dtype == float
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# test clipping
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clipped_action = action_space.clip_action_to_space(np.array([-10, 10, 2, 5]))
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assert np.all(clipped_action == np.array([-5, 5, 2, 5]))
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# invalid bounds
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with pytest.raises(ValueError):
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action_space = BoxActionSpace(4, np.array([-5, -1, -0.5, 0]), -1, ["a", "b", "c", "d"])
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# TODO: test descriptions
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@pytest.mark.unit_test
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def test_multiselect():
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action_space = MultiSelectActionSpace(4, 2, ["a", "b", "c", "d"])
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for i in range(100):
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action = action_space.sample()
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assert action.shape == (4,)
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assert np.sum(action) <= 2
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# check that descriptions of multiple actions are working
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description = action_space.get_description(np.array([1, 0, 1, 0]))
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assert description == "a + c"
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description = action_space.get_description(np.array([0, 0, 0, 0]))
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assert description == "no-op"
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@pytest.mark.unit_test
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def test_attention():
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low = np.array([-1, -2, -3, -4])
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high = np.array([1, 2, 3, 4])
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action_space = AttentionActionSpace(4, low=low, high=high)
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for i in range(100):
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action = action_space.sample()
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assert len(action) == 2
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assert action[0].shape == (4,)
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assert action[1].shape == (4,)
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assert np.all(action[0] <= action[1])
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assert np.all(action[0] >= low)
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assert np.all(action[1] < high)
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@pytest.mark.unit_test
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def test_goal():
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# TODO: test goal action space
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pass
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@pytest.mark.unit_test
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def test_agent_selection():
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action_space = AgentSelection(10)
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assert action_space.shape == 1
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assert action_space.high == 9
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assert action_space.low == 0
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with pytest.raises(ValueError):
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assert action_space.get_description(10)
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assert action_space.get_description(0) == "0"
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@pytest.mark.unit_test
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def test_observation_space():
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observation_space = ObservationSpace(np.array([1, 10]), -10, 10)
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# testing that contains works
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assert observation_space.contains(np.ones([1, 10]))
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assert not observation_space.contains(np.ones([2, 10]))
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assert not observation_space.contains(np.ones([1, 10]) * 100)
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assert not observation_space.contains(np.ones([1, 1, 10]))
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# is_valid_index
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assert observation_space.is_valid_index(np.array([0, 9]))
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assert observation_space.is_valid_index(np.array([0, 0]))
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assert not observation_space.is_valid_index(np.array([1, 8]))
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assert not observation_space.is_valid_index(np.array([0, 10]))
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assert not observation_space.is_valid_index(np.array([-1, 6]))
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@pytest.mark.unit_test
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def test_image_observation_space():
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# TODO: test image observation space
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pass
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@pytest.mark.unit_test
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def test_measurements_observation_space():
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# empty measurements space
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measurements_space = VectorObservationSpace(0)
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# vector space
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measurements_space = VectorObservationSpace(3, measurements_names=['a', 'b', 'c'])
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@pytest.mark.unit_test
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def test_reward_space():
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# TODO: test reward space
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pass
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# def test_discrete_to_linspace_action_space_map():
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# box = BoxActionSpace(2, np.array([0, 0]), np.array([10, 10]))
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# linspace = BoxDiscretization(box, [5, 3])
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# assert np.all(linspace.actions == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]))
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# assert np.all(linspace.target_actions ==
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# np.array([[0.0, 0.0], [0.0, 5.0], [0.0, 10.0],
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# [2.5, 0.0], [2.5, 5.0], [2.5, 10.0],
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# [5.0, 0.0], [5.0, 5.0], [5.0, 10.0],
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# [7.5, 0.0], [7.5, 5.0], [7.5, 10.0],
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# [10.0, 0.0], [10.0, 5.0], [10.0, 10.0]]))
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#
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#
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# def test_discrete_to_attention_action_space_map():
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# attention = AttentionActionSpace(2, np.array([0, 0]), np.array([10, 10]))
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# linspace = AttentionDiscretization(attention, 2)
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# assert np.all(linspace.actions == np.array([0, 1, 2, 3]))
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# assert np.all(linspace.target_actions ==
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# np.array(
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# [[[0., 0.], [5., 5.]],
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# [[0., 5.], [5., 10.]],
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# [[5., 0.], [10., 5.]],
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# [[5., 5.], [10., 10.]]])
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# )
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if __name__ == "__main__":
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test_observation_space()
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test_discrete_to_linspace_action_space_map()
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test_discrete_to_attention_action_space_map()
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