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。
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
 
 
 
 
 

219 行
7.6 KiB

import unittest.mock as mock
import pytest
import os
import numpy as np
from mlagents.tf_utils import tf
import yaml
from mlagents.trainers.bc.models import BehavioralCloningModel
import mlagents.trainers.tests.mock_brain as mb
from mlagents.trainers.bc.policy import BCPolicy
from mlagents.trainers.bc.offline_trainer import BCTrainer
from mlagents.envs.environment import UnityEnvironment
from mlagents.envs.mock_communicator import MockCommunicator
from mlagents.trainers.tests.mock_brain import make_brain_parameters
@pytest.fixture
def dummy_config():
return yaml.safe_load(
"""
hidden_units: 32
learning_rate: 3.0e-4
num_layers: 1
use_recurrent: false
sequence_length: 32
memory_size: 32
batches_per_epoch: 1
batch_size: 32
summary_freq: 2000
max_steps: 4000
"""
)
def create_bc_trainer(dummy_config, is_discrete=False):
mock_env = mock.Mock()
if is_discrete:
mock_brain = mb.create_mock_pushblock_brain()
mock_braininfo = mb.create_mock_braininfo(
num_agents=12, num_vector_observations=70
)
else:
mock_brain = mb.create_mock_3dball_brain()
mock_braininfo = mb.create_mock_braininfo(
num_agents=12, num_vector_observations=8
)
mb.setup_mock_unityenvironment(mock_env, mock_brain, mock_braininfo)
env = mock_env()
trainer_parameters = dummy_config
trainer_parameters["summary_path"] = "tmp"
trainer_parameters["model_path"] = "tmp"
trainer_parameters["demo_path"] = (
os.path.dirname(os.path.abspath(__file__)) + "/test.demo"
)
trainer = BCTrainer(
mock_brain, trainer_parameters, training=True, load=False, seed=0, run_id=0
)
trainer.demonstration_buffer = mb.simulate_rollout(env, trainer.policy, 100)
return trainer, env
def test_bc_trainer_step(dummy_config):
trainer, env = create_bc_trainer(dummy_config)
# Test get_step
assert trainer.get_step == 0
# Test update policy
trainer.update_policy()
assert len(trainer.stats["Losses/Cloning Loss"]) > 0
# Test increment step
trainer.increment_step(1)
assert trainer.step == 1
def test_bc_trainer_add_proc_experiences(dummy_config):
trainer, env = create_bc_trainer(dummy_config)
# Test add_experiences
returned_braininfo = env.step()
trainer.add_experiences(
returned_braininfo, returned_braininfo, {}
) # Take action outputs is not used
for agent_id in returned_braininfo["Ball3DBrain"].agents:
assert trainer.evaluation_buffer[agent_id].last_brain_info is not None
assert trainer.episode_steps[agent_id] > 0
assert trainer.cumulative_rewards[agent_id] > 0
# Test process_experiences by setting done
returned_braininfo["Ball3DBrain"].local_done = 12 * [True]
trainer.process_experiences(returned_braininfo, returned_braininfo)
for agent_id in returned_braininfo["Ball3DBrain"].agents:
assert trainer.episode_steps[agent_id] == 0
assert trainer.cumulative_rewards[agent_id] == 0
def test_bc_trainer_end_episode(dummy_config):
trainer, env = create_bc_trainer(dummy_config)
returned_braininfo = env.step()
trainer.add_experiences(
returned_braininfo, returned_braininfo, {}
) # Take action outputs is not used
trainer.process_experiences(returned_braininfo, returned_braininfo)
# Should set everything to 0
trainer.end_episode()
for agent_id in returned_braininfo["Ball3DBrain"].agents:
assert trainer.episode_steps[agent_id] == 0
assert trainer.cumulative_rewards[agent_id] == 0
@mock.patch("mlagents.envs.environment.UnityEnvironment.executable_launcher")
@mock.patch("mlagents.envs.environment.UnityEnvironment.get_communicator")
def test_bc_policy_evaluate(mock_communicator, mock_launcher, dummy_config):
tf.reset_default_graph()
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0
)
env = UnityEnvironment(" ")
brain_infos = env.reset()
brain_info = brain_infos[env.external_brain_names[0]]
trainer_parameters = dummy_config
model_path = env.external_brain_names[0]
trainer_parameters["model_path"] = model_path
trainer_parameters["keep_checkpoints"] = 3
policy = BCPolicy(
0, env.brains[env.external_brain_names[0]], trainer_parameters, False
)
run_out = policy.evaluate(brain_info)
assert run_out["action"].shape == (3, 2)
env.close()
def test_cc_bc_model():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = BehavioralCloningModel(
make_brain_parameters(discrete_action=False, visual_inputs=0)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.sample_action, model.policy]
feed_dict = {
model.batch_size: 2,
model.sequence_length: 1,
model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
}
sess.run(run_list, feed_dict=feed_dict)
# env.close()
def test_dc_bc_model():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = BehavioralCloningModel(
make_brain_parameters(discrete_action=True, visual_inputs=0)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.sample_action, model.action_probs]
feed_dict = {
model.batch_size: 2,
model.dropout_rate: 1.0,
model.sequence_length: 1,
model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.action_masks: np.ones([2, 2]),
}
sess.run(run_list, feed_dict=feed_dict)
def test_visual_dc_bc_model():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = BehavioralCloningModel(
make_brain_parameters(discrete_action=True, visual_inputs=2)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.sample_action, model.action_probs]
feed_dict = {
model.batch_size: 2,
model.dropout_rate: 1.0,
model.sequence_length: 1,
model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.visual_in[0]: np.ones([2, 40, 30, 3]),
model.visual_in[1]: np.ones([2, 40, 30, 3]),
model.action_masks: np.ones([2, 2]),
}
sess.run(run_list, feed_dict=feed_dict)
def test_visual_cc_bc_model():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = BehavioralCloningModel(
make_brain_parameters(discrete_action=False, visual_inputs=2)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.sample_action, model.policy]
feed_dict = {
model.batch_size: 2,
model.sequence_length: 1,
model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.visual_in[0]: np.ones([2, 40, 30, 3]),
model.visual_in[1]: np.ones([2, 40, 30, 3]),
}
sess.run(run_list, feed_dict=feed_dict)
if __name__ == "__main__":
pytest.main()