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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103 行
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import unittest.mock as mock
import pytest
import numpy as np
import tensorflow as tf
from unitytrainers.bc.models import BehavioralCloningModel
from unityagents import UnityEnvironment
def test_cc_bc_model():
c_action_c_state_start = '''{
"AcademyName": "RealFakeAcademy",
"resetParameters": {},
"brainNames": ["RealFakeBrain"],
"externalBrainNames": ["RealFakeBrain"],
"logPath":"RealFakePath",
"apiNumber":"API-3",
"brainParameters": [{
"vectorObservationSize": 3,
"numStackedVectorObservations": 2,
"vectorActionSize": 2,
"memorySize": 0,
"cameraResolutions": [],
"vectorActionDescriptions": ["",""],
"vectorActionSpaceType": 1,
"vectorObservationSpaceType": 1
}]
}'''.encode()
tf.reset_default_graph()
with mock.patch('subprocess.Popen'):
with mock.patch('socket.socket') as mock_socket:
with mock.patch('glob.glob') as mock_glob:
# End of mock
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_glob.return_value = ['FakeLaunchPath']
mock_socket.return_value.accept.return_value = (mock_socket, 0)
mock_socket.recv.return_value.decode.return_value = c_action_c_state_start
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()
def test_dc_bc_model():
d_action_c_state_start = '''{
"AcademyName": "RealFakeAcademy",
"resetParameters": {},
"brainNames": ["RealFakeBrain"],
"externalBrainNames": ["RealFakeBrain"],
"logPath":"RealFakePath",
"apiNumber":"API-3",
"brainParameters": [{
"vectorObservationSize": 3,
"numStackedVectorObservations": 2,
"vectorActionSize": 2,
"memorySize": 0,
"cameraResolutions": [{"width":30,"height":40,"blackAndWhite":false}],
"vectorActionDescriptions": ["",""],
"vectorActionSpaceType": 0,
"vectorObservationSpaceType": 1
}]
}'''.encode()
tf.reset_default_graph()
with mock.patch('subprocess.Popen'):
with mock.patch('socket.socket') as mock_socket:
with mock.patch('glob.glob') as mock_glob:
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_glob.return_value = ['FakeLaunchPath']
mock_socket.return_value.accept.return_value = (mock_socket, 0)
mock_socket.recv.return_value.decode.return_value = d_action_c_state_start
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.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])}
sess.run(run_list, feed_dict=feed_dict)
env.close()
if __name__ == '__main__':
pytest.main()