Deep Q-Network 学习笔记（四）—— 改进②：double dqn

这篇没搞懂。。。这里只对实现做记录。

修改的地方也只是在上一篇的基础上，在“记忆回放”函数里，计算 target Q 时取值做下调整即可。

    def experience_replay(self):
        """
        记忆回放。
        :return:
        """
        # 检查是否替换 target_net 参数
        if self.learn_step_counter % self.network.replace_target_stepper == 0:
            self.network.replace_target_params()

        # 随机选择一小批记忆样本。
        batch = self.BATCH if self.memory_counter > self.BATCH else self.memory_counter
        minibatch = random.sample(self.replay_memory_store, batch)

        batch_state = None
        batch_action = None
        batch_reward = None
        batch_next_state = None
        batch_done = None

        for index in range(len(minibatch)):
            if batch_state is None:
                batch_state = minibatch[index][0]
            elif batch_state is not None:
                batch_state = np.vstack((batch_state, minibatch[index][0]))

            if batch_action is None:
                batch_action = minibatch[index][1]
            elif batch_action is not None:
                batch_action = np.vstack((batch_action, minibatch[index][1]))

            if batch_reward is None:
                batch_reward = minibatch[index][2]
            elif batch_reward is not None:
                batch_reward = np.vstack((batch_reward, minibatch[index][2]))

            if batch_next_state is None:
                batch_next_state = minibatch[index][3]
            elif batch_next_state is not None:
                batch_next_state = np.vstack((batch_next_state, minibatch[index][3]))

            if batch_done is None:
                batch_done = minibatch[index][4]
            elif batch_done is not None:
                batch_done = np.vstack((batch_done, minibatch[index][4]))

        q_next = self.network.get_next_q(batch_next_state)
        q_eval4next = self.network.get_q(batch_next_state)

        # q_eval 得出的最高奖励动作。
        max_act4next = np.argmax(q_eval4next, axis=1)

        q_target = []
        for i in range(len(minibatch)):
            # Double DQN 选择 q_next 依据 q_eval 选出的动作。
            selected_q_next = q_next[i, max_act4next]
            max_q = selected_q_next[0]

            # 当前即时得分。
            current_reward = batch_reward[i][0]

            # # 游戏是否结束。
            # current_done = batch_done[i][0]

            # 更新 Q 值。
            q_value = current_reward + self.gamma * max_q

            # 当得分小于 -1 时，表示走了不可走的位置。
            if current_reward <= -1:
                q_target.append(current_reward)
            else:
                q_target.append(q_value)

        self.network.train(batch_state, q_target, batch_action)

        self.learn_step_counter += 1

完整代码

神经网络部分:

import tensorflow as tf
import numpy as np

class DeepQNetwork:
    # q_eval 网络状态输入参数。
    q_eval_input = None

    # q_eval 网络动作输入参数。
    q_action_input = None

    # q_eval 网络中 q_target 的输入参数。
    q_eval_target = None

    # q_eval 网络输出结果。
    q_eval_output = None

    # q_eval 网络输出的结果中的最优得分。
    q_predict = None

    # q_eval 网络输出的结果中当前选择的动作得分。
    reward_action = None

    # q_eval 网络损失函数。
    loss = None

    # q_eval 网络训练。
    train_op = None

    # q_target 网络状态输入参数。
    q_target_input = None

    # q_target 网络输出结果。
    q_target_output = None

    # 更换 target_net 的步数。
    replace_target_stepper = 0

    def __init__(self, input_num, output_num, learning_rate=0.001, replace_target_stepper=300, session=None):
        self.learning_rate = learning_rate
        self.INPUT_NUM = input_num
        self.OUTPUT_NUM = output_num
        self.replace_target_stepper = replace_target_stepper

        self.create()

        if session is None:
            self.session = tf.InteractiveSession()
            self.session.run(tf.initialize_all_variables())

    def create(self):
        neuro_layer_1 = 3
        w_init = tf.random_normal_initializer(0, 0.3)
        b_init = tf.constant_initializer(0.1)

        # -------------- 创建 eval 神经网络, 及时提升参数 -------------- #
        self.q_eval_input = tf.placeholder(shape=[None, self.INPUT_NUM], dtype=tf.float32, name="q_eval_input")
        self.q_action_input = tf.placeholder(shape=[None, self.OUTPUT_NUM], dtype=tf.float32)
        self.q_eval_target = tf.placeholder(shape=[None], dtype=tf.float32, name="q_target")

        with tf.variable_scope("eval_net"):
            q_name = ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES]

            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.INPUT_NUM, neuro_layer_1], initializer=w_init, collections=q_name)
                b1 = tf.get_variable('b1', [1, neuro_layer_1], initializer=b_init, collections=q_name)
                l1 = tf.nn.relu(tf.matmul(self.q_eval_input, w1) + b1)

            with tf.variable_scope('l2'):
                w2 = tf.get_variable('w2', [neuro_layer_1, self.OUTPUT_NUM], initializer=w_init, collections=q_name)
                b2 = tf.get_variable('b2', [1, self.OUTPUT_NUM], initializer=b_init, collections=q_name)
                self.q_eval_output = tf.matmul(l1, w2) + b2
                self.q_predict = tf.argmax(self.q_eval_output, 1)

        with tf.variable_scope('loss'):
            # 取出当前动作的得分。
            self.reward_action = tf.reduce_sum(tf.multiply(self.q_eval_output, self.q_action_input),
                                               reduction_indices=1)
            self.loss = tf.reduce_mean(tf.square((self.q_eval_target - self.reward_action)))

        with tf.variable_scope('train'):
            self.train_op = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.loss)

        # -------------- 创建 target 神经网络, 及时提升参数 -------------- #
        self.q_target_input = tf.placeholder(shape=[None, self.INPUT_NUM], dtype=tf.float32, name="q_target_input")

        with tf.variable_scope("target_net"):
            t_name = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]

            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.INPUT_NUM, neuro_layer_1], initializer=w_init, collections=t_name)
                b1 = tf.get_variable('b1', [1, neuro_layer_1], initializer=b_init, collections=t_name)
                l1 = tf.nn.relu(tf.matmul(self.q_target_input, w1) + b1)

            with tf.variable_scope('l2'):
                w2 = tf.get_variable('w2', [neuro_layer_1, self.OUTPUT_NUM], initializer=w_init, collections=t_name)
                b2 = tf.get_variable('b2', [1, self.OUTPUT_NUM], initializer=b_init, collections=t_name)
                self.q_target_output = tf.matmul(l1, w2) + b2

    def replace_target_params(self):
        """
        使用 Tensorflow 中的 assign 功能替换 target_net 所有参数。
        :return:
        """
        # 提取 target_net 的参数。
        t_params = tf.get_collection('target_net_params')
        # 提取 eval_net 的参数。
        e_params = tf.get_collection('eval_net_params')
        # 更新 target_net 参数。
        self.session.run([tf.assign(t, e) for t, e in zip(t_params, e_params)])

    def get_q(self, input_data):
        return self.session.run(self.q_eval_output, {self.q_eval_input: input_data})

    def get_next_q(self, input_data):
        return self.session.run(self.q_target_output, {self.q_target_input: input_data})

    def get_predict(self, input_data):
        return np.max(self.get_q(input_data))

    def get_action(self, input_data):
        return np.argmax(self.get_q(input_data))

    def train(self, input_data, y_, action_input):
        _, cost = self.session.run([self.train_op, self.loss],
                                   feed_dict={self.q_eval_input: input_data,
                                              self.q_action_input: action_input,
                                              self.q_eval_target: y_})
        return cost

主逻辑实现：

import numpy as np
from collections import deque
import random
from q_network import DeepQNetwork

class Agent:

    r = np.array([[-1, -1, -1, -1, 0, -1],
                  [-1, -1, -1, 0, -1, 100.0],
                  [-1, -1, -1, 0, -1, -1],
                  [-1, 0, 0, -1, 0, -1],
                  [0, -1, -1, 1, -1, 100],
                  [-1, 0, -1, -1, 0, 100],
                  ])

    # 神经网络。
    network = None

    def __init__(self):
        # 执行步数。
        self.step_index = 0

        # 状态数。
        self.STATE_NUM = 6

        # 动作数。
        self.ACTION_NUM = 6

        # 记忆上限。
        self.memory_size = 5000

        # 当前记忆数。
        self.memory_counter = 0

        # 保存观察到的执行过的行动的存储器，即：曾经经历过的记忆。
        self.replay_memory_store = deque()

        # 训练之前观察多少步。
        self.OBSERVE = 5000

        # 训练步数统计。
        self.learn_step_counter = 0

        # 选取的小批量训练样本数。
        self.BATCH = 20

        # γ经验折损率。
        self.gamma = 0.9

        # -------------------- 探索策略 -------------------- #
        # epsilon 的最小值，当 epsilon 小于该值时，将不在随机选择行为。
        self.FINAL_EPSILON = 0.0001

        # epsilon 的初始值，epsilon 逐渐减小。
        self.INITIAL_EPSILON = 0.1

        # epsilon 衰减的总步数。
        self.EXPLORE = 3000000.

        # 探索模式计数。
        self.epsilon = 0
        # -------------------- 探索策略 -------------------- #

        # 生成神经网络。
        self.network = DeepQNetwork(input_num=self.STATE_NUM,
                                    output_num=self.ACTION_NUM,
                                    learning_rate=0.001,
                                    replace_target_stepper=300,
                                    session=None)

        # 生成一个状态矩阵（6 X 6），每一行代表一个状态。
        self.state_list = np.identity(self.STATE_NUM)

        # 生成一个动作矩阵。
        self.action_list = np.identity(self.ACTION_NUM)

    def select_action(self, current_state_index):
        """
        根据策略选择动作。
        :param current_state_index:
        :return:
        """
        # 获得当前状态。
        current_state = self.state_list[current_state_index:current_state_index + 1]

        # 根据当前状态获得在 Q 网络中最有价值的动作，并返回动作序号。
        current_action_index = self.network.get_action(current_state)

        if np.random.uniform() < self.epsilon:
            current_action_index = np.random.randint(0, self.ACTION_NUM)

        # 开始训练后，在 epsilon 小于一定的值之前，将逐步减小 epsilon。
        if self.step_index > self.OBSERVE and self.epsilon > self.FINAL_EPSILON:
            self.epsilon -= (self.INITIAL_EPSILON - self.FINAL_EPSILON) / self.EXPLORE

        return current_action_index

    def save_store(self, current_state_index, current_action_index, current_reward, next_state_index, done):
            """
            保存记忆。
            :param current_state_index: 当前状态 index。
            :param current_action_index: 动作 index。
            :param current_reward: 奖励。
            :param next_state_index: 下一个状态 index。
            :param done: 是否结束。
            :return:
            """
            current_state = self.state_list[current_state_index:current_state_index + 1]
            current_action = self.action_list[current_action_index:current_action_index + 1]
            next_state = self.state_list[next_state_index:next_state_index + 1]
            # 记忆动作(当前状态， 当前执行的动作， 当前动作的得分，下一个状态)。
            self.replay_memory_store.append((
                current_state,
                current_action,
                current_reward,
                next_state,
                done))

            # 如果超过记忆的容量，则将最久远的记忆移除。
            if len(self.replay_memory_store) > self.memory_size:
                self.replay_memory_store.popleft()

            self.memory_counter += 1

    def run_game(self, state_index, action_index):
        """
        执行动作。
        :param state_index: 当前状态。
        :param action_index: 执行的动作。
        :return:
        """
        reward = self.r[state_index][action_index]

        next_state = action_index

        done = False

        if action_index == 5:
            done = True

        return next_state, reward, done

    def experience_replay(self):
        """
        记忆回放。
        :return:
        """
        # 检查是否替换 target_net 参数
        if self.learn_step_counter % self.network.replace_target_stepper == 0:
            self.network.replace_target_params()

        # 随机选择一小批记忆样本。
        batch = self.BATCH if self.memory_counter > self.BATCH else self.memory_counter
        minibatch = random.sample(self.replay_memory_store, batch)

        batch_state = None
        batch_action = None
        batch_reward = None
        batch_next_state = None
        batch_done = None

        for index in range(len(minibatch)):
            if batch_state is None:
                batch_state = minibatch[index][0]
            elif batch_state is not None:
                batch_state = np.vstack((batch_state, minibatch[index][0]))

            if batch_action is None:
                batch_action = minibatch[index][1]
            elif batch_action is not None:
                batch_action = np.vstack((batch_action, minibatch[index][1]))

            if batch_reward is None:
                batch_reward = minibatch[index][2]
            elif batch_reward is not None:
                batch_reward = np.vstack((batch_reward, minibatch[index][2]))

            if batch_next_state is None:
                batch_next_state = minibatch[index][3]
            elif batch_next_state is not None:
                batch_next_state = np.vstack((batch_next_state, minibatch[index][3]))

            if batch_done is None:
                batch_done = minibatch[index][4]
            elif batch_done is not None:
                batch_done = np.vstack((batch_done, minibatch[index][4]))

　　　　
        q_next = self.network.get_next_q(batch_next_state)
        q_eval4next = self.network.get_q(batch_next_state)

        # q_eval 得出的最高奖励动作。
        max_act4next = np.argmax(q_eval4next, axis=1)

        q_target = []
        for i in range(len(minibatch)):
            # Double DQN 选择 q_next 依据 q_eval 选出的动作。
            selected_q_next = q_next[i, max_act4next]
            max_q = selected_q_next[0]

            # 当前即时得分。
            current_reward = batch_reward[i][0]

            # # 游戏是否结束。
            # current_done = batch_done[i][0]

            # 更新 Q 值。
            q_value = current_reward + self.gamma * max_q

            # 当得分小于 -1 时，表示走了不可走的位置。
            if current_reward <= -1:
                q_target.append(current_reward)
            else:
                q_target.append(q_value)

        self.network.train(batch_state, q_target, batch_action)

        self.learn_step_counter += 1

    def train(self):
        """
        训练。
        :return:
        """
        # 初始化当前状态。
        current_state = np.random.randint(0, self.ACTION_NUM - 1)
        self.epsilon = self.INITIAL_EPSILON

        while True:
            # 选择动作。
            action = self.select_action(current_state)

            # 执行动作，得到：下一个状态，执行动作的得分，是否结束。
            next_state, reward, done = self.run_game(current_state, action)

            # 保存记忆。
            self.save_store(current_state, action, reward, next_state, done)

            # 先观察一段时间累积足够的记忆在进行训练。
            if self.step_index > self.OBSERVE:
                self.experience_replay()

            if self.step_index - self.OBSERVE > 15000:
                break

            if done:
                current_state = np.random.randint(0, self.ACTION_NUM - 1)
            else:
                current_state = next_state

            self.step_index += 1

    def pay(self):
        """
        运行并测试。
        :return:
        """
        self.train()

        # 显示 R 矩阵。
        print(self.r)

        for index in range(5):

            start_room = index

            print("#############################", "Agent 在", start_room, "开始行动", "#############################")

            current_state = start_room

            step = 0

            target_state = 5

            while current_state != target_state:
                next_state = self.network.get_action(self.state_list[current_state:current_state + 1])

                print("Agent 由", current_state, "号房间移动到了", next_state, "号房间")

                current_state = next_state

                step += 1

            print("Agent 在", start_room, "号房间开始移动了", step, "步到达了目标房间 5")

            print("#############################", "Agent 在", 5, "结束行动", "#############################")

if __name__ == "__main__":
    agent = Agent()
    agent.pay()