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
 
 
 
 
 

285 行
13 KiB

import unittest.mock as mock
import pytest
import numpy as np
import tensorflow as tf
from unitytrainers.ppo.models import PPOModel
from unitytrainers.ppo.trainer import discount_rewards
from unityagents import UnityEnvironment
from .mock_communicator import MockCommunicator
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_cc_vector(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 = PPOModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.probs, model.value, model.entropy,
model.learning_rate]
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('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_cc_visual(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 = PPOModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.probs, model.value, model.entropy,
model.learning_rate]
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()
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_dc_visual(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 = PPOModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate]
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()
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_dc_vector(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 = PPOModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate]
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('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_dc_vector_rnn(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(' ')
memory_size = 128
model = PPOModel(env.brains["RealFakeBrain"], use_recurrent=True, m_size=memory_size)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate, model.memory_out]
feed_dict = {model.batch_size: 1,
model.sequence_length: 2,
model.prev_action: [0, 0],
model.memory_in: np.zeros((1, memory_size)),
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('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_cc_vector_rnn(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(' ')
memory_size = 128
model = PPOModel(env.brains["RealFakeBrain"], use_recurrent=True, m_size=memory_size)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate, model.memory_out]
feed_dict = {model.batch_size: 1,
model.sequence_length: 2,
model.memory_in: np.zeros((1, memory_size)),
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('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_dc_vector_curio(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 = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate, model.intrinsic_reward]
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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.action_holder: [0, 0]}
sess.run(run_list, feed_dict=feed_dict)
env.close()
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_cc_vector_curio(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 = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate, model.intrinsic_reward]
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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.output: [[0.0, 0.0], [0.0, 0.0]]}
sess.run(run_list, feed_dict=feed_dict)
env.close()
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_dc_visual_curio(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 = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate, model.intrinsic_reward]
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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.action_holder: [0, 0],
model.visual_in[0]: np.ones([2, 40, 30, 3]),
model.visual_in[1]: np.ones([2, 40, 30, 3]),
model.next_visual_in[0]: np.ones([2, 40, 30, 3]),
model.next_visual_in[1]: np.ones([2, 40, 30, 3])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_ppo_model_cc_visual_curio(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 = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, model.all_probs, model.value, model.entropy,
model.learning_rate, model.intrinsic_reward]
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.next_vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.output: [[0.0, 0.0], [0.0, 0.0]],
model.visual_in[0]: np.ones([2, 40, 30, 3]),
model.visual_in[1]: np.ones([2, 40, 30, 3]),
model.next_visual_in[0]: np.ones([2, 40, 30, 3]),
model.next_visual_in[1]: np.ones([2, 40, 30, 3])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
def test_rl_functions():
rewards = np.array([0.0, 0.0, 0.0, 1.0])
gamma = 0.9
returns = discount_rewards(rewards, gamma, 0.0)
np.testing.assert_array_almost_equal(returns, np.array([0.729, 0.81, 0.9, 1.0]))
if __name__ == '__main__':
pytest.main()