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136 行
4.0 KiB
136 行
4.0 KiB
import pytest
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import numpy as np
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from mlagents_envs.base_env import (
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DecisionSteps,
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TerminalSteps,
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ActionType,
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BehaviorSpec,
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)
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def test_decision_steps():
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ds = DecisionSteps(
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obs=[np.array(range(12), dtype=np.float32).reshape(3, 4)],
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reward=np.array(range(3), dtype=np.float32),
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agent_id=np.array(range(10, 13), dtype=np.int32),
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action_mask=[np.zeros((3, 4), dtype=np.bool)],
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)
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assert ds.agent_id_to_index[10] == 0
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assert ds.agent_id_to_index[11] == 1
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assert ds.agent_id_to_index[12] == 2
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with pytest.raises(KeyError):
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assert ds.agent_id_to_index[-1] == -1
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mask_agent = ds[10].action_mask
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assert isinstance(mask_agent, list)
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assert len(mask_agent) == 1
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assert np.array_equal(mask_agent[0], np.zeros((4), dtype=np.bool))
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for agent_id in ds:
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assert ds.agent_id_to_index[agent_id] in range(3)
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def test_empty_decision_steps():
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specs = BehaviorSpec(
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observation_shapes=[(3, 2), (5,)],
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action_type=ActionType.CONTINUOUS,
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action_shape=3,
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)
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ds = DecisionSteps.empty(specs)
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assert len(ds.obs) == 2
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assert ds.obs[0].shape == (0, 3, 2)
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assert ds.obs[1].shape == (0, 5)
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def test_terminal_steps():
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ts = TerminalSteps(
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obs=[np.array(range(12), dtype=np.float32).reshape(3, 4)],
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reward=np.array(range(3), dtype=np.float32),
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agent_id=np.array(range(10, 13), dtype=np.int32),
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interrupted=np.array([1, 0, 1], dtype=np.bool),
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)
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assert ts.agent_id_to_index[10] == 0
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assert ts.agent_id_to_index[11] == 1
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assert ts.agent_id_to_index[12] == 2
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assert ts[10].interrupted
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assert not ts[11].interrupted
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assert ts[12].interrupted
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with pytest.raises(KeyError):
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assert ts.agent_id_to_index[-1] == -1
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for agent_id in ts:
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assert ts.agent_id_to_index[agent_id] in range(3)
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def test_empty_terminal_steps():
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specs = BehaviorSpec(
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observation_shapes=[(3, 2), (5,)],
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action_type=ActionType.CONTINUOUS,
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action_shape=3,
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)
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ts = TerminalSteps.empty(specs)
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assert len(ts.obs) == 2
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assert ts.obs[0].shape == (0, 3, 2)
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assert ts.obs[1].shape == (0, 5)
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def test_specs():
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specs = BehaviorSpec(
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observation_shapes=[(3, 2), (5,)],
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action_type=ActionType.CONTINUOUS,
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action_shape=3,
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)
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assert specs.discrete_action_branches is None
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assert specs.action_size == 3
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assert specs.create_empty_action(5).shape == (5, 3)
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assert specs.create_empty_action(5).dtype == np.float32
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specs = BehaviorSpec(
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observation_shapes=[(3, 2), (5,)],
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action_type=ActionType.DISCRETE,
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action_shape=(3,),
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)
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assert specs.discrete_action_branches == (3,)
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assert specs.action_size == 1
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assert specs.create_empty_action(5).shape == (5, 1)
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assert specs.create_empty_action(5).dtype == np.int32
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def test_action_generator():
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# Continuous
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action_len = 30
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specs = BehaviorSpec(
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observation_shapes=[(5,)],
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action_type=ActionType.CONTINUOUS,
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action_shape=action_len,
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)
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zero_action = specs.create_empty_action(4)
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assert np.array_equal(zero_action, np.zeros((4, action_len), dtype=np.float32))
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random_action = specs.create_random_action(4)
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assert random_action.dtype == np.float32
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assert random_action.shape == (4, action_len)
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assert np.min(random_action) >= -1
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assert np.max(random_action) <= 1
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# Discrete
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action_shape = (10, 20, 30)
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specs = BehaviorSpec(
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observation_shapes=[(5,)],
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action_type=ActionType.DISCRETE,
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action_shape=action_shape,
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)
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zero_action = specs.create_empty_action(4)
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assert np.array_equal(zero_action, np.zeros((4, len(action_shape)), dtype=np.int32))
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random_action = specs.create_random_action(4)
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assert random_action.dtype == np.int32
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assert random_action.shape == (4, len(action_shape))
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assert np.min(random_action) >= 0
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for index, branch_size in enumerate(action_shape):
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assert np.max(random_action[:, index]) < branch_size
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