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76 行
2.4 KiB
76 行
2.4 KiB
from mlagents.trainers.tf_policy import TFPolicy
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from mlagents_envs.base_env import BatchedStepResult, AgentGroupSpec
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from mlagents.trainers.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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mock_brain.vector_observation_space_size = 1
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mock_brain.vector_action_space_size = [1]
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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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class FakePolicy(TFPolicy):
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def create_tf_graph(self):
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pass
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def get_trainable_variables(self):
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return []
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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 = FakePolicy(test_seed, basic_mock_brain(), basic_params())
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# Doesn't really matter what this is
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dummy_groupspec = AgentGroupSpec([(1,)], "continuous", 1)
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no_agent_step = BatchedStepResult.empty(dummy_groupspec)
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result = policy.get_action(no_agent_step)
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assert result == ActionInfo.empty()
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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 = FakePolicy(test_seed, basic_mock_brain(), basic_params())
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policy.evaluate = MagicMock(return_value={})
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policy.save_memories = MagicMock()
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step_with_agents = BatchedStepResult(
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[],
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np.array([], dtype=np.float32),
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np.array([False], dtype=np.bool),
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np.array([], dtype=np.bool),
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np.array([0]),
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None,
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)
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result = policy.get_action(step_with_agents, worker_id=0)
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assert result == ActionInfo(None, None, {}, [0])
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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 = FakePolicy(test_seed, basic_mock_brain(), basic_params())
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policy_eval_out = {
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"action": np.array([1.0], dtype=np.float32),
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"memory_out": np.array([[2.5]], dtype=np.float32),
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"value": np.array([1.1], dtype=np.float32),
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}
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policy.evaluate = MagicMock(return_value=policy_eval_out)
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step_with_agents = BatchedStepResult(
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[],
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np.array([], dtype=np.float32),
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np.array([False], dtype=np.bool),
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np.array([], dtype=np.bool),
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np.array([0]),
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None,
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)
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result = policy.get_action(step_with_agents)
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expected = ActionInfo(
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policy_eval_out["action"], policy_eval_out["value"], policy_eval_out, [0]
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)
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assert result == expected
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