Deep Q-Learning深度增强学习（代码篇）

搭建DQN

初始化

#动作数量self.n_actions #状态数量self.n_features#learning_rate学习速率self.lr#Q-learning中reward衰减因子self.gamma#e-greedy的选择概率最大值self.epsilon_max #更新Q现实网络参数的步骤数self.replace_target_iter#存储记忆的数量self.memory_size#每次从记忆库中取的样本数量self.batch_size = batch_sizeself.epsilon_increment = e_greedy_incrementself.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max#学习的步骤self.learn_step_counter#记忆库，此刻的n_feature + 下一步的n_feature + reward + actionself.memory = np.zeros((self.memory_size, n_features * 2 + 2))#利用Q目标的参数替换Q估计中的参数t_params = tf.get_collection('target_net_params')e_params = tf.get_collection('eval_net_params')#生成了一个tensorflow操作列表[tf.assign(t1,e1), tf.assign(t2,e2), tf.assign(t3,e3)]self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]

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构建神经网络

构造Q估计神经网络

def _build_net(self):    #输入    self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s')    #Q现实输入    self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target')    with tf.variable_scope('eval_net'):        #collection        c_names = ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES]         #神经元数量        n_l1 =  10        #权值        w_initializer = tf.random_normal_initializer(0., 0.3)        #偏置        b_initializer = tf.constant_initializer(0.1)        #第一层神经元                with 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)        #第二层神经元        with 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) + b2        #基于Q估计与Q现实，构造loss-function        with tf.variable_scope('loss'):            self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))        #训练        with tf.variable_scope('train'):            self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)

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构造Q现实神经网络(该段代码紧接着上段，属于_build_net()函数)

    #输入    self.s_sub = tf.placeholder(tf.float32, [None, self.n_features], name='s_sub')        with tf.variable_scope('target_net'):        #collection        c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]        #第一层神经元        with 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)        #第二层神经元        with 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) + b2

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存储状态信息

    def store_transition(self, s, a, r, s_):        if not hasattr(self, 'memory_counter'):            self.memory_counter = 0        #状态信息list ==> [x, y]        #[action, reward]动作与奖励信息合并为list        #下一步状态信息 ==> [x_next, y_next]        transition = np.hstack((s, [a, r], s_))        #hstack的结果为 ==> [x, y, a, r, x_next, y_next]        #每过memory_size，替换存储值        index = self.memory_counter % self.memory_size        #memory为二维列表，transition为一行向量，插入index行中        self.memory[index, :] = transition        self.memory_counter += 1

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选择动作action

    def choose_action(self, observation):        # 将observation的list[x, y]转为行向量[[x, y]]        observation = observation[np.newaxis, :]        if np.random.uniform() < self.epsilon:            # 得到每个action的q的估计值            actions_value = self.sess.run(self.q_eval, feed_dict={self.s: observation})            # 选择q值最大的action            action = np.argmax(actions_value)        else:            action = np.random.randint(0, self.n_actions)        return action

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增强学习过程

    def learn(self):        #更换参数        if self.learn_step_counter % self.replace_target_iter == 0:            self.sess.run(self.replace_target_op)        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)        #从memory中抽取一个记忆值，一个行向量        #[x, y, a, r, x_next, y_next]        batch_memory = self.memory[sample_index, :]        q_next, q_eval = self.sess.run(         [self.q_next, self.q_eval],            feed_dict={             self.s_: batch_memory[:, -self.n_features:],  # fixed params             self.s: batch_memory[:, :self.n_features],  # newest params            })        q_target = q_eval.copy()        batch_index = np.arange(self.batch_size, dtype=np.int32)        eval_act_index = batch_memory[:, self.n_features].astype(int)        reward = batch_memory[:, self.n_features + 1]        q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)        #训练网络        _, 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 epsilon        self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max        self.learn_step_counter += 1

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举例说明上述过程

数据结构

• action=3
• n_feature=2
• batch_size=2

q-eval结构

action_0 action_1 action_2 1 2 1 2 3 2

行：每一个样本
列：每一个action对应的Q值

q-next,q-target与q-eval结构相同

batch-index样本索引

一维list ==> [0, 1] #长度：bactch_size

eval_act_index每个样本对应的action的值，也就是每个样本列的索引

一维list ==> [1, 0]

reward每个样本对应的reward的值

一维list ==> [1, 2]

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过程

1. 将q-eval的值赋给q-target
2. 利用Q-learning算法，计算每一个样本的对应action的q值
   
   • 样本0，采取了action=0，真实的q值为-1
   • 样本1，采取了action=2，真实的q值为-2
3. 更新q-target中的值

action_0 action_1 action_2 -1 2 1 2 3 -2

4. 利用更新后的q-target与q-eval之间的差值进行训练

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仿真过程

def run_maze():    # 游戏的每一个回合需要的步数    step = 0    # 游戏的回合    for episode in range(300):        # 初始化观察值        observation = env.reset()        while True:            # 开始环境仿真            env.render()            # 选择动作            action = RL.choose_action(observation)            # 加入动作后，环境进行仿真            # 获取了执行action后，下一步的观测值observation            # 获取了奖励reward            # 游戏是否结束标志done            observation_, reward, done = env.step(action)            # 存储样本            RL.store_transition(observation, action, reward, observation_)            if (step > 200) and (step % 5 == 0):            # 随机抽取样本，网络进行学习                RL.learn()            # 交换观测值            observation = observation_            # 判断游戏是否结束            if done:                break            step += 1