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qlearning_network.py
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qlearning_network.py
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import numpy as np
import chess_rule as rule
from util import add_print_time_fun, print_use_time
from record import Record
from value_network import ValueNetwork, train_once
import logging
import util
logger = logging.getLogger('train')
class DQN(ValueNetwork):
@staticmethod
def load_model(model_file):
from keras.models import load_model
from keras.optimizers import Adam, SGD
from keras.regularizers import l2
logger.info('load model in DQN')
model = load_model(model_file)
# 这里中途修改了一下输出层的正则化参数和SGD的学习率
'''
l = 1e-3
for layer in model.layers:
layer.kernel_regularizer = l2(l)
layer.bias_regularizer = l2(l)
l = 0.001
out = model.get_layer(index=-1)
out.kernel_regularizer = l2(l)
out.bias_regularizer = l2(l)
'''
model.optimizer = SGD(lr=1e-3, decay=1e-5)
return model
def create_model(self):
from keras.models import Sequential, load_model
from keras.layers import Dense, Convolution2D, Flatten
from keras.optimizers import Adam, SGD
from keras.regularizers import l2
# 定义顺序模型
model = Sequential()
l = 1e-3
# 第一个卷积层
model.add(Convolution2D(
filters=100, # 卷积核/滤波器个数
kernel_size=3, # 卷积窗口大小
input_shape=(5,5,10), # 输入平面的形状
strides=1, # 步长
padding='same', # padding方式 same:保持图大小不变/valid
activation=self.hidden_activation, # 激活函数
kernel_regularizer=l2(l),
bias_regularizer=l2(l)
))
def create_conv_layer(filters=50, kernel_size=3):
return Convolution2D(filters=filters,
kernel_size=kernel_size,
strides=1,
padding='same',
activation=self.hidden_activation,
kernel_regularizer=l2(l),
bias_regularizer=l2(l)
)
# 第二个卷积层
model.add(create_conv_layer())
# 第三个卷积层
model.add(create_conv_layer())
# 第四个卷积层
model.add(create_conv_layer())
# 第五个卷积层
model.add(create_conv_layer(filters=25, kernel_size=1))
# 把卷积层的输出扁平化为1维
model.add(Flatten())
# 全连接层
model.add(Dense(units=100,
activation=self.hidden_activation,
kernel_regularizer=l2(l),
bias_regularizer=l2(l)
))
l = 0.01
# 输出Q值
model.add(Dense(units=1,
activation='sigmoid',
kernel_initializer='zeros',
kernel_regularizer=l2(l),
bias_initializer='zeros',
bias_regularizer=l2(l)
))
# 定义优化器
# opt = Adam(lr=1e-4)
opt = SGD(lr=self.lr, decay=1e-5)
# loss function
loss = 'mse' # if self.output_activation == 'linear' else 'binary_crossentropy' if self.output_activation == 'sigmoid' else None
model.compile(optimizer=opt, loss=loss)
return model
def policy(self, board, player):
return self.policy_by_epsilon_greedy(board, player)
@staticmethod
def feature(board, from_, action):
"""
第一视角的棋局特征
:param board: 棋盘
:param from_: 走哪颗子
:param action: 动作，向哪个方向走
:return: 当前动作的特征(5x5xN)
"""
player = board[from_]
to_ = tuple(np.add(from_, rule.actions_move[action]))
# 棋盘特征:空白-己方棋子-对方棋子
space = (board == 0).astype(np.int8).reshape((5, 5, 1))
self = (board == player).astype(np.int8).reshape((5, 5, 1))
opponent = (board == -player).astype(np.int8).reshape((5, 5, 1))
# 动作特征
from_location = np.zeros((5,5,1))
from_location[from_] = 1
to_location = np.zeros((5,5,1))
to_location[to_] = 1
# 走子后的棋盘
board = board.copy()
result,_ = rule.move(board, from_, to_)
space2 = (board == 0).astype(np.int8).reshape((5, 5, 1))
self2 = (board == player).astype(np.int8).reshape((5, 5, 1))
opponent2 = (board == -player).astype(np.int8).reshape((5, 5, 1))
# 走子后是否赢棋
is_win = np.ones((5,5,1)) if result == rule.WIN else np.zeros((5,5,1))
# 偏置
bias = np.ones((5, 5, 1))
return np.concatenate((space, self, opponent, from_location, to_location, space2, self2, opponent2, is_win, bias), axis=2)
@staticmethod
def load(modelfile, epsilon=0.3):
return DQN(epsilon=epsilon, model=util.load_model(modelfile))
def train():
logging.info('...begin...')
add_print_time_fun(['simulate', 'train_once'])
hidden_activation = 'relu'
activation = 'sigmoid' # linear, selu, sigmoid
begin = 6390000
model_file = 'model/qlearning_network/DQN_fixed_sigmoid_555_%05dw.model' % np.ceil(begin / 10000)
n_ = DQN(epsilon=1, epsilon_decay=0.2, output_activation=activation, model_file=model_file)
n0 = DQN(epsilon=1, epsilon_decay=0.2, output_activation=activation, hidden_activation=hidden_activation)
n1 = DQN(epsilon=1, epsilon_decay=0.2, output_activation=activation, hidden_activation=hidden_activation)
n0.copy(n_)
n1.copy(n_)
episode = 300000
for i in range(1, episode+1):
train_once(n0, n1, i, activation, init='random')
if i % 1000 == 0:
n0.save_model('model/qlearning_network/DQN_random_%s_%05dw.model' % (activation, np.ceil((i+begin) / 10000)))
begin += episode
n0.episode = 1
n1.episode = 1
for i in range(1, episode*10 + 1):
records = train_once(n0, n1, i, activation, init='fixed', copy_period=1)
if i % 1000 == 0:
records.save('records/train/qlearning_network/1st_')
if i % 1000 == 0:
logger.info('model/qlearning_network/DQN_fixed_%s_555_%05dw.model' % (activation, np.ceil((i+begin) / 10000)))
n0.save_model('model/qlearning_network/DQN_fixed_%s_555_%05dw.model' % (activation, np.ceil((i+begin) / 10000)))
if __name__ == '__main__':
import logging.config
logging.config.fileConfig('logging.conf')
# _main()
train()