Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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from mlagents.trainers.tf_policy import TFPolicy
from mlagents_envs.base_env import BatchedStepResult, AgentGroupSpec
from mlagents.trainers.action_info import ActionInfo
from unittest.mock import MagicMock
import numpy as np
def basic_mock_brain():
mock_brain = MagicMock()
mock_brain.vector_action_space_type = "continuous"
mock_brain.vector_observation_space_size = 1
mock_brain.vector_action_space_size = [1]
return mock_brain
def basic_params():
return {"use_recurrent": False, "model_path": "my/path"}
class FakePolicy(TFPolicy):
def create_tf_graph(self):
pass
def get_trainable_variables(self):
return []
def test_take_action_returns_empty_with_no_agents():
test_seed = 3
policy = FakePolicy(test_seed, basic_mock_brain(), basic_params())
# Doesn't really matter what this is
dummy_groupspec = AgentGroupSpec([(1,)], "continuous", 1)
no_agent_step = BatchedStepResult.empty(dummy_groupspec)
result = policy.get_action(no_agent_step)
assert result == ActionInfo.empty()
def test_take_action_returns_nones_on_missing_values():
test_seed = 3
policy = FakePolicy(test_seed, basic_mock_brain(), basic_params())
policy.evaluate = MagicMock(return_value={})
policy.save_memories = MagicMock()
step_with_agents = BatchedStepResult(
[],
np.array([], dtype=np.float32),
np.array([False], dtype=np.bool),
np.array([], dtype=np.bool),
np.array([0]),
None,
)
result = policy.get_action(step_with_agents, worker_id=0)
assert result == ActionInfo(None, None, {}, [0])
def test_take_action_returns_action_info_when_available():
test_seed = 3
policy = FakePolicy(test_seed, basic_mock_brain(), basic_params())
policy_eval_out = {
"action": np.array([1.0], dtype=np.float32),
"memory_out": np.array([[2.5]], dtype=np.float32),
"value": np.array([1.1], dtype=np.float32),
}
policy.evaluate = MagicMock(return_value=policy_eval_out)
step_with_agents = BatchedStepResult(
[],
np.array([], dtype=np.float32),
np.array([False], dtype=np.bool),
np.array([], dtype=np.bool),
np.array([0]),
None,
)
result = policy.get_action(step_with_agents)
expected = ActionInfo(
policy_eval_out["action"], policy_eval_out["value"], policy_eval_out, [0]
)
assert result == expected