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53 行
1.7 KiB
53 行
1.7 KiB
from mlagents.trainers.tf_policy import TFPolicy
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from mlagents.envs.brain import BrainInfo
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from mlagents.envs.action_info import ActionInfo
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from unittest.mock import MagicMock
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import numpy as np
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def basic_mock_brain():
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mock_brain = MagicMock()
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mock_brain.vector_action_space_type = "continuous"
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return mock_brain
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def basic_params():
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return {"use_recurrent": False, "model_path": "my/path"}
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def test_take_action_returns_empty_with_no_agents():
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test_seed = 3
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policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
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no_agent_brain_info = BrainInfo([], [], [], agents=[])
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result = policy.get_action(no_agent_brain_info)
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assert result == ActionInfo([], [], [], None, None)
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def test_take_action_returns_nones_on_missing_values():
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test_seed = 3
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policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
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policy.evaluate = MagicMock(return_value={})
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brain_info_with_agents = BrainInfo([], [], [], agents=["an-agent-id"])
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result = policy.get_action(brain_info_with_agents)
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assert result == ActionInfo(None, None, None, None, {})
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def test_take_action_returns_action_info_when_available():
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test_seed = 3
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policy = TFPolicy(test_seed, basic_mock_brain(), basic_params())
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policy_eval_out = {
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"action": np.array([1.0]),
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"memory_out": np.array([2.5]),
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"value": np.array([1.1]),
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}
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policy.evaluate = MagicMock(return_value=policy_eval_out)
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brain_info_with_agents = BrainInfo([], [], [], agents=["an-agent-id"])
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result = policy.get_action(brain_info_with_agents)
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expected = ActionInfo(
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policy_eval_out["action"],
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policy_eval_out["memory_out"],
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None,
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policy_eval_out["value"],
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policy_eval_out,
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)
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assert result == expected
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