def _build_net(self):# ------------------ build evaluate_net ------------------self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s')  # inputself.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target')  # for calculating losswith tf.variable_scope('eval_net'):# n_l1 第一隐层的神经元个数   w b 初始化c_names, n_l1, w_initializer, b_initializer = \['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 10, \tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1)  # config of layers# first layer. collections is used later when assign to target netwith tf.variable_scope('l1'):w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)l1 = tf.nn.relu(tf.matmul(self.s, w1) + b1)# second layer. collections is used later when assign to target netwith tf.variable_scope('l2'):w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)self.q_eval = tf.matmul(l1, w2) + b2with tf.variable_scope('loss'):self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))with tf.variable_scope('train'):self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)# ------------------ build target_net ------------------self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_')    # inputwith tf.variable_scope('target_net'):# c_names(collections_names) are the collections to store variablesc_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]# first layer. collections is used later when assign to target netwith tf.variable_scope('l1'):w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)l1 = tf.nn.relu(tf.matmul(self.s_, w1) + b1)# second layer. collections is used later when assign to target netwith tf.variable_scope('l2'):w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)self.q_next = tf.matmul(l1, w2) + b2def store_transition(self, s, a, r, s_):# 存储记忆,固定大小的记忆库if not hasattr(self, 'memory_counter'):self.memory_counter = 0transition = np.hstack((s, [a, r], s_))# replace the old memory with new memoryindex = self.memory_counter % self.memory_sizeself.memory[index, :] = transitionself.memory_counter += 1def choose_action(self, observation):# q learning 选择动作 e贪心observation = observation[np.newaxis, :]if np.random.uniform() < self.epsilon:actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})action = np.argmax(actions_value)else:action = np.random.randint(0, self.n_actions)return actiondef learn(self):# 每隔一定步数更新，代表积累了一定的经验才进行总结，这样显得不那么武断，对应到神经网络，就是更新效率高，容易收敛if self.learn_step_counter % self.replace_target_iter == 0:     # 更新神经网络参数
            self.sess.run(self.replace_target_op)print('\ntarget_params_replaced\n')##### 取样本## memory 是 初始化一个 memory_size 的数据表，当记忆大于这个表时，表已被填满，随机从表中选择记忆即可，## 当记忆小于这个表时，表未被填满，只能从记忆里随机选样本if self.memory_counter > self.memory_size:sample_index = np.random.choice(self.memory_size, size=self.batch_size)else:sample_index = np.random.choice(self.memory_counter, size=self.batch_size)batch_memory = self.memory[sample_index, :]##### 训练神经网络# 利用神经网络 计算 q_next 下个状态的q值，q_eval 当前状态的q值q_next, q_eval = self.sess.run([self.q_next, self.q_eval], feed_dict={self.s_: batch_memory[:, -self.n_features:],   # 下一个状态self.s: batch_memory[:, :self.n_features],     # 当前状态
            })q_target = q_eval.copy()        # q_eval 是q 估计### 更新 q learning 的 q表## 每个真实实验有个动作和奖励batch_index = np.arange(self.batch_size, dtype=np.int32)eval_act_index = batch_memory[:, self.n_features].astype(int)   # 动作reward = batch_memory[:, self.n_features + 1]                   # 奖励## 更新q表q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)        # q 现实# 注意这里的动作当前状态选择的动作，而不是下个状态基于贪心的动作# train eval network_, self.cost = self.sess.run([self._train_op, self.loss],feed_dict={self.s: batch_memory[:, :self.n_features],self.q_target: q_target})self.cost_his.append(self.cost)# increasing epsilonself.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_maxself.learn_step_counter += 1