您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
71 行
2.5 KiB
71 行
2.5 KiB
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)
|