云计算任务调度仿真03

前面陆续分享了基于policy gradient和DQN实现的深度强化学习任务调度仿真，上次的DQN没有实现fix-qtarget和experience replay，这次再分享实现了这两个方法的DQN任务调度仿真。
经验重放，定义存储和存放次序，这里也可以自行修改

    def store_transition(self, s, a, r, s_):
        #经验重放
        one_hot_action = np.zeros(self.n_actions)
        one_hot_action[a] = 1
        self.replay_buffer.append((s, one_hot_action, r, s_))
        if len(self.replay_buffer) > self.memory_size:
            self.replay_buffer.popleft()

在学习过程定期更新网络

    def learn(self):
        # 定期替换网络参数
        if self.learn_step_counter % self.replace_target_iter == 0:
            self.sess.run(self.replace_target_op)
            # print('-------------target_params_replaced------------------')

        # sample batch memory from all memory: [s, a, r, s_]，采样学习，批量学习
        minibatch = random.sample(self.replay_buffer, self.batch_size)
        state_batch = [data[0] for data in minibatch]
        action_batch = [data[1] for data in minibatch]
        next_state_batch = [data[3] for data in minibatch]

        # calculate target q-value batch
        q_next_batch = self.sess.run(self.q_next, feed_dict={
   self.s_: next_state_batch})
        q_real_batch = []
        for i in range(self.batch_size):
            q_real_batch.append(minibatch[i][2] + self.gamma * np.max(q_next_batch[i]))
        # train eval network
        self.sess.run(self._train_op, feed_dict={
   
            self.s: state_batch,
            self.action_input: action_batch,
            self.q_target: q_real_batch
        })

        # increasing epsilon
        if self.epsilon < self.epsilon_max:
            self.epsilon = self.epsilon + self.epsilon_increment
        else:
            self.epsilon = self.epsilon_max
        # print('epsilon:', self.epsilon)
        self.learn_step_counter += 1

运行效果

还有一些对比算法

代码是基于TensorFlow1.x实现的，可以在此基础上再修改优化，完整的代码可根据名字去GitHub上下载获取。