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.policy.tf_policy import TFPolicy
from mlagents_envs.base_env import DecisionSteps, BehaviorSpec
from mlagents.trainers.action_info import ActionInfo
from unittest.mock import MagicMock
from mlagents.trainers.settings import TrainerSettings
import numpy as np
from mlagents_envs.base_env import ActionSpec
def basic_behavior_spec():
dummy_actionspec = ActionSpec.create_continuous(1)
dummy_groupspec = BehaviorSpec([(1,)], dummy_actionspec)
return dummy_groupspec
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
behavior_spec = basic_behavior_spec()
policy = FakePolicy(test_seed, behavior_spec, TrainerSettings(), "output")
no_agent_step = DecisionSteps.empty(behavior_spec)
result = policy.get_action(no_agent_step)
assert result == ActionInfo.empty()
def test_take_action_returns_nones_on_missing_values():
test_seed = 3
behavior_spec = basic_behavior_spec()
policy = FakePolicy(test_seed, behavior_spec, TrainerSettings(), "output")
policy.evaluate = MagicMock(return_value={})
policy.save_memories = MagicMock()
step_with_agents = DecisionSteps(
[], np.array([], dtype=np.float32), 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
behavior_spec = basic_behavior_spec()
policy = FakePolicy(test_seed, behavior_spec, TrainerSettings(), "output")
policy_eval_out = {
"action": {"continuous_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 = DecisionSteps(
[], np.array([], dtype=np.float32), 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
def test_convert_version_string():
result = TFPolicy._convert_version_string("200.300.100")
assert result == (200, 300, 100)
# Test dev versions
result = TFPolicy._convert_version_string("200.300.100.dev0")
assert result == (200, 300, 100)