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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import pytest
import numpy as np
import yaml
from mlagents.trainers.ghost.trainer import GhostTrainer
from mlagents.trainers.ghost.controller import GhostController
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.brain import BrainParameters
from mlagents.trainers.agent_processor import AgentManagerQueue
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
@pytest.fixture
def dummy_config():
return yaml.safe_load(
"""
trainer: ppo
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
normalize: true
memory_size: 8
curiosity_strength: 0.0
curiosity_enc_size: 1
summary_path: test
model_path: test
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
self_play:
window: 5
play_against_current_self_ratio: 0.5
save_steps: 1000
swap_steps: 1000
"""
)
VECTOR_ACTION_SPACE = [1]
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 513
NUM_AGENTS = 12
@pytest.mark.parametrize("use_discrete", [True, False])
def test_load_and_set(dummy_config, use_discrete):
mock_brain = mb.setup_mock_brain(
use_discrete,
False,
vector_action_space=VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
discrete_action_space=DISCRETE_ACTION_SPACE,
)
trainer_params = dummy_config
trainer = PPOTrainer(mock_brain.brain_name, 0, trainer_params, True, False, 0, "0")
trainer.seed = 1
policy = trainer.create_policy(mock_brain.brain_name, mock_brain)
policy.create_tf_graph()
trainer.seed = 20 # otherwise graphs are the same
to_load_policy = trainer.create_policy(mock_brain.brain_name, mock_brain)
to_load_policy.create_tf_graph()
to_load_policy.init_load_weights()
weights = policy.get_weights()
load_weights = to_load_policy.get_weights()
try:
for w, lw in zip(weights, load_weights):
np.testing.assert_array_equal(w, lw)
except AssertionError:
pass
to_load_policy.load_weights(weights)
load_weights = to_load_policy.get_weights()
for w, lw in zip(weights, load_weights):
np.testing.assert_array_equal(w, lw)
def test_process_trajectory(dummy_config):
brain_params_team0 = BrainParameters(
brain_name="test_brain?team=0",
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
brain_name = BehaviorIdentifiers.from_name_behavior_id(
brain_params_team0.brain_name
).brain_name
brain_params_team1 = BrainParameters(
brain_name="test_brain?team=1",
vector_observation_space_size=1,
camera_resolutions=[],
vector_action_space_size=[2],
vector_action_descriptions=[],
vector_action_space_type=0,
)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
controller = GhostController(100)
trainer = GhostTrainer(
ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
)
# first policy encountered becomes policy trained by wrapped PPO
parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(
brain_params_team0.brain_name
)
policy = trainer.create_policy(parsed_behavior_id0, brain_params_team0)
trainer.add_policy(parsed_behavior_id0, policy)
trajectory_queue0 = AgentManagerQueue(brain_params_team0.brain_name)
trainer.subscribe_trajectory_queue(trajectory_queue0)
# Ghost trainer should ignore this queue because off policy
parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(
brain_params_team1.brain_name
)
policy = trainer.create_policy(parsed_behavior_id1, brain_params_team1)
trainer.add_policy(parsed_behavior_id1, policy)
trajectory_queue1 = AgentManagerQueue(brain_params_team1.brain_name)
trainer.subscribe_trajectory_queue(trajectory_queue1)
time_horizon = 15
trajectory = make_fake_trajectory(
length=time_horizon,
max_step_complete=True,
vec_obs_size=1,
num_vis_obs=0,
action_space=[2],
)
trajectory_queue0.put(trajectory)
trainer.advance()
# Check that trainer put trajectory in update buffer
assert trainer.trainer.update_buffer.num_experiences == 15
trajectory_queue1.put(trajectory)
trainer.advance()
# Check that ghost trainer ignored off policy queue
assert trainer.trainer.update_buffer.num_experiences == 15
# Check that it emptied the queue
assert trajectory_queue1.empty()
def test_publish_queue(dummy_config):
brain_params_team0 = BrainParameters(
brain_name="test_brain?team=0",
vector_observation_space_size=8,
camera_resolutions=[],
vector_action_space_size=[1],
vector_action_descriptions=[],
vector_action_space_type=0,
)
parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(
brain_params_team0.brain_name
)
brain_name = parsed_behavior_id0.brain_name
brain_params_team1 = BrainParameters(
brain_name="test_brain?team=1",
vector_observation_space_size=8,
camera_resolutions=[],
vector_action_space_size=[1],
vector_action_descriptions=[],
vector_action_space_type=0,
)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
controller = GhostController(100)
trainer = GhostTrainer(
ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
)
# First policy encountered becomes policy trained by wrapped PPO
# This queue should remain empty after swap snapshot
policy = trainer.create_policy(parsed_behavior_id0, brain_params_team0)
trainer.add_policy(parsed_behavior_id0, policy)
policy_queue0 = AgentManagerQueue(brain_params_team0.brain_name)
trainer.publish_policy_queue(policy_queue0)
# Ghost trainer should use this queue for ghost policy swap
parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(
brain_params_team1.brain_name
)
policy = trainer.create_policy(parsed_behavior_id1, brain_params_team1)
trainer.add_policy(parsed_behavior_id1, policy)
policy_queue1 = AgentManagerQueue(brain_params_team1.brain_name)
trainer.publish_policy_queue(policy_queue1)
# check ghost trainer swap pushes to ghost queue and not trainer
assert policy_queue0.empty() and policy_queue1.empty()
trainer._swap_snapshots()
assert policy_queue0.empty() and not policy_queue1.empty()
# clear
policy_queue1.get_nowait()
mock_brain = mb.setup_mock_brain(
False,
False,
vector_action_space=VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
discrete_action_space=DISCRETE_ACTION_SPACE,
)
buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, mock_brain)
# Mock out reward signal eval
buffer["extrinsic_rewards"] = buffer["environment_rewards"]
buffer["extrinsic_returns"] = buffer["environment_rewards"]
buffer["extrinsic_value_estimates"] = buffer["environment_rewards"]
buffer["curiosity_rewards"] = buffer["environment_rewards"]
buffer["curiosity_returns"] = buffer["environment_rewards"]
buffer["curiosity_value_estimates"] = buffer["environment_rewards"]
buffer["advantages"] = buffer["environment_rewards"]
trainer.trainer.update_buffer = buffer
# when ghost trainer advance and wrapped trainer buffers full
# the wrapped trainer pushes updated policy to correct queue
assert policy_queue0.empty() and policy_queue1.empty()
trainer.advance()
assert not policy_queue0.empty() and policy_queue1.empty()
if __name__ == "__main__":
pytest.main()