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
 
 
 
 
 

147 行
5.9 KiB

import unittest.mock as mock
import pytest
import numpy as np
import tensorflow as tf
import yaml
from mlagents.trainers.bc.models import BehavioralCloningModel
from mlagents.trainers.bc.policy import BCPolicy
from mlagents.envs import UnityEnvironment
from tests.mock_communicator import MockCommunicator
@pytest.fixture
def dummy_config():
return yaml.load(
'''
hidden_units: 128
learning_rate: 3.0e-4
num_layers: 2
use_recurrent: false
sequence_length: 32
memory_size: 32
''')
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_bc_policy_evaluate(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
brain_infos = env.reset()
brain_info = brain_infos[env.brain_names[0]]
trainer_parameters = dummy_config()
graph_scope = env.brain_names[0]
trainer_parameters['graph_scope'] = graph_scope
policy = BCPolicy(0, env.brains[env.brain_names[0]], trainer_parameters, sess)
init = tf.global_variables_initializer()
sess.run(init)
run_out = policy.evaluate(brain_info)
assert run_out['action'].shape == (3, 2)
env.close()
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_cc_bc_model(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
model = BehavioralCloningModel(env.brains["RealFakeBrain"])
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()
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_dc_bc_model(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=0)
env = UnityEnvironment(' ')
model = BehavioralCloningModel(env.brains["RealFakeBrain"])
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)
env.close()
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_visual_dc_bc_model(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=2)
env = UnityEnvironment(' ')
model = BehavioralCloningModel(env.brains["RealFakeBrain"])
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)
env.close()
@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_visual_cc_bc_model(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=2)
env = UnityEnvironment(' ')
model = BehavioralCloningModel(env.brains["RealFakeBrain"])
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)
env.close()
if __name__ == '__main__':
pytest.main()