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Python|Python 还能实现哪些 AI 游戏？附上代码一起来一把( 三 )

然后定义整体的类变量DQN ， 分别定义初始化和训练函数 ， 其中网络层哪里主要就是神经网络层的调用 。 然后在训练函数里面记录当前动作和数据加载入优化器中达到模型训练效果 。
其中代码如下：
def __init__(self, options):self.options = optionsself.num_action = options['num_action']self.lr = options['lr']self.modelDir = options['modelDir']self.init_prob = options['init_prob']self.end_prob = options['end_prob']self.OBSERVE = options['OBSERVE']self.EXPLORE = options['EXPLORE']self.action_interval = options['action_interval']self.REPLAY_MEMORY = options['REPLAY_MEMORY']self.gamma = options['gamma']self.batch_size = options['batch_size']self.save_interval = options['save_interval']self.logfile = options['logfile']self.is_train = options['is_train']'''训练网络'''def train(self, session):x, q_values_ph = self.create_networkaction_now_ph = tf.placeholder('float', [None, self.num_action])target_q_values_ph = tf.placeholder('float', [None])# 计算lossloss = self.compute_loss(q_values_ph, action_now_ph, target_q_values_ph)# 优化目标trainStep = tf.train.AdamOptimizer(self.lr).minimize(loss)# 游戏gameState = PongGame# 用于记录数据dataDeque = deque# 当前的动作action_now = np.zeros(self.num_action)action_now[0] = 1# 初始化游戏状态x_now, reward, terminal = gameState.update_frame(action_now)x_now = cv2.cvtColor(cv2.resize(x_now, (80, 80)), cv2.COLOR_BGR2GRAY)_, x_now = cv2.threshold(x_now, 127, 255, cv2.THRESH_BINARY)scene_now = np.stack((x_now, )*4, axis=2)# 读取和保存checkpointsaver = tf.train.Saversession.run(tf.global_variables_initializer)checkpoint = tf.train.get_checkpoint_state(self.modelDir)if checkpoint and checkpoint.model_checkpoint_path:saver.restore(session, checkpoint.model_checkpoint_path)print('[INFO]: Load %s successfully...' % checkpoint.model_checkpoint_path)else:print('[INFO]: No weights found, start to train a new model...')prob = self.init_probnum_frame = 0logF = open(self.logfile, 'a')while True:q_values = q_values_ph.eval(feed_dict={x: [scene_now]})action_idx = get_action_idx(q_values=q_values,prob=prob,num_frame=num_frame,OBSERVE=self.OBSERVE,num_action=self.num_action)action_now = np.zeros(self.num_action)action_now[action_idx] = 1prob = down_prob(prob=prob,num_frame=num_frame,OBSERVE=self.OBSERVE,EXPLORE=self.EXPLORE,init_prob=self.init_prob,end_prob=self.end_prob)for _ in range(self.action_interval):scene_next, reward, terminal = self.next_frame(action_now=action_now,scene_now=scene_now, gameState=gameState)scene_now = scene_nextdataDeque.append((scene_now, action_now, reward, scene_next, terminal))if len(dataDeque) > self.REPLAY_MEMORY:dataDeque.popleftloss_now = Noneif (num_frame > self.OBSERVE):minibatch = random.sample(dataDeque, self.batch_size)scene_now_batch = [mb[0] for mb in minibatch]action_batch = [mb[1] for mb in minibatch]reward_batch = [mb[2] for mb in minibatch]scene_next_batch = [mb[3] for mb in minibatch]q_values_batch = q_values_ph.eval(feed_dict={x: scene_next_batch})target_q_values = self.compute_target_q_values(reward_batch, q_values_batch, minibatch)trainStep.run(feed_dict={target_q_values_ph: target_q_values,action_now_ph: action_batch,x: scene_now_batch})loss_now = session.run(loss, feed_dict={target_q_values_ph: target_q_values,action_now_ph: action_batch,x: scene_now_batch})num_frame += 1if num_frame % self.save_interval == 0:name = 'DQN_Pong'saver.save(session, os.path.join(self.modelDir, name), global_step=num_frame)log_content = ': %s, : %s, : %s, : %s, : %s, : %s' % (str(num_frame), str(prob), str(action_idx), str(reward), str(np.max(q_values)), str(loss_now))logF.write(log_content + '\n')print(log_content)logF.close'''创建网络'''def create_network(self):'''W_conv1 = self.init_weight_variable([9, 9, 4, 16])b_conv1 = self.init_bias_variable([16])W_conv2 = self.init_weight_variable([7, 7, 16, 32])b_conv2 = self.init_bias_variable([32])W_conv3 = self.init_weight_variable([5, 5, 32, 32])b_conv3 = self.init_bias_variable([32])W_conv4 = self.init_weight_variable([5, 5, 32, 64])b_conv4 = self.init_bias_variable([64])W_conv5 = self.init_weight_variable([3, 3, 64, 64])b_conv5 = self.init_bias_variable([64])'''W_conv1 = self.init_weight_variable([8, 8, 4, 32])b_conv1 = self.init_bias_variable([32])W_conv2 = self.init_weight_variable([4, 4, 32, 64])b_conv2 = self.init_bias_variable([64])W_conv3 = self.init_weight_variable([3, 3, 64, 64])b_conv3 = self.init_bias_variable([64])# 5 * 5 * 64 = 1600W_fc1 = self.init_weight_variable([1600, 512])b_fc1 = self.init_bias_variable([512])W_fc2 = self.init_weight_variable([512, self.num_action])b_fc2 = self.init_bias_variable([self.num_action])# input placeholderx = tf.placeholder('float', [None, 80, 80, 4])'''conv1 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(x, W_conv1, 4) + b_conv1, training=self.is_train, momentum=0.9))conv2 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv1, W_conv2, 2) + b_conv2, training=self.is_train, momentum=0.9))conv3 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv2, W_conv3, 2) + b_conv3, training=self.is_train, momentum=0.9))conv4 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv3, W_conv4, 1) + b_conv4, training=self.is_train, momentum=0.9))conv5 = tf.nn.relu(tf.layers.batch_normalization(self.conv2D(conv4, W_conv5, 1) + b_conv5, training=self.is_train, momentum=0.9))flatten = tf.reshape(conv5, [-1, 1600])'''conv1 = tf.nn.relu(self.conv2D(x, W_conv1, 4) + b_conv1)pool1 = self.maxpool(conv1)conv2 = tf.nn.relu(self.conv2D(pool1, W_conv2, 2) + b_conv2)conv3 = tf.nn.relu(self.conv2D(conv2, W_conv3, 1) + b_conv3)flatten = tf.reshape(conv3, [-1, 1600])fc1 = tf.nn.relu(tf.layers.batch_normalization(tf.matmul(flatten, W_fc1) + b_fc1, training=self.is_train, momentum=0.9))fc2 = tf.matmul(fc1, W_fc2) + b_fc2return x, fc2


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