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