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 unittest import mock
import pytest
import mlagents.trainers.tests.mock_brain as mb
import numpy as np
from mlagents.trainers.agent_processor import (
AgentProcessor,
AgentManager,
AgentManagerQueue,
)
from mlagents.trainers.action_info import ActionInfo
from mlagents.trainers.trajectory import Trajectory
from mlagents.trainers.stats import StatsReporter
def create_mock_brain():
mock_brain = mb.create_mock_brainparams(
vector_action_space_type="continuous",
vector_action_space_size=[2],
vector_observation_space_size=8,
number_visual_observations=1,
)
return mock_brain
def create_mock_policy():
mock_policy = mock.Mock()
mock_policy.reward_signals = {}
mock_policy.retrieve_memories.return_value = np.zeros((1, 1), dtype=np.float32)
mock_policy.retrieve_previous_action.return_value = np.zeros(
(1, 1), dtype=np.float32
)
return mock_policy
@pytest.mark.parametrize("num_vis_obs", [0, 1, 2], ids=["vec", "1 viz", "2 viz"])
def test_agentprocessor(num_vis_obs):
policy = create_mock_policy()
tqueue = mock.Mock()
name_behavior_id = "test_brain_name"
processor = AgentProcessor(
policy,
name_behavior_id,
max_trajectory_length=5,
stats_reporter=StatsReporter("testcat"),
)
fake_action_outputs = {
"action": [0.1, 0.1],
"entropy": np.array([1.0], dtype=np.float32),
"learning_rate": 1.0,
"pre_action": [0.1, 0.1],
"log_probs": [0.1, 0.1],
}
mock_step = mb.create_mock_batchedstep(
num_agents=2,
num_vector_observations=8,
action_shape=[2],
num_vis_observations=num_vis_obs,
)
fake_action_info = ActionInfo(
action=[0.1, 0.1],
value=[0.1, 0.1],
outputs=fake_action_outputs,
agent_ids=mock_step.agent_id,
)
processor.publish_trajectory_queue(tqueue)
# This is like the initial state after the env reset
processor.add_experiences(mock_step, 0, ActionInfo([], [], {}, []))
for _ in range(5):
processor.add_experiences(mock_step, 0, fake_action_info)
# Assert that two trajectories have been added to the Trainer
assert len(tqueue.put.call_args_list) == 2
# Assert that the trajectory is of length 5
trajectory = tqueue.put.call_args_list[0][0][0]
assert len(trajectory.steps) == 5
# Assert that the AgentProcessor is empty
assert len(processor.experience_buffers[0]) == 0
# Test empty BatchedStepResult
mock_step = mb.create_mock_batchedstep(
num_agents=0,
num_vector_observations=8,
action_shape=[2],
num_vis_observations=num_vis_obs,
)
processor.add_experiences(mock_step, 0, ActionInfo([], [], {}, []))
# Assert that the AgentProcessor is still empty
assert len(processor.experience_buffers[0]) == 0
def test_agent_manager():
policy = create_mock_policy()
name_behavior_id = "test_brain_name"
manager = AgentManager(
policy,
name_behavior_id,
max_trajectory_length=5,
stats_reporter=StatsReporter("testcat"),
)
assert len(manager.trajectory_queues) == 1
assert isinstance(manager.trajectory_queues[0], AgentManagerQueue)
def test_agent_manager_queue():
queue = AgentManagerQueue(behavior_id="testbehavior")
trajectory = mock.Mock(spec=Trajectory)
assert queue.empty()
queue.put(trajectory)
assert not queue.empty()
queue_traj = queue.get_nowait()
assert isinstance(queue_traj, Trajectory)
assert queue.empty()