Deep Q-Network 学习笔记(六)—— 改进④:dueling dqn

这篇同样是完全没看懂 Orz,这里只做实现记录。。

要改动的地方只是在神经网络的最后一层做下调整即可。

    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_eval_target = tf.placeholder(shape=[None, self.OUTPUT_NUM], dtype=tf.float32, name="q_target")

        def _dueling_builder(input_data, name, nl_1, w_initializer, b_initializer):
            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.INPUT_NUM, nl_1], initializer=w_initializer, collections=name)
                b1 = tf.get_variable('b1', [1, nl_1], initializer=b_initializer, collections=name)
                l1 = tf.nn.relu(tf.matmul(input_data, w1) + b1)

            with tf.variable_scope('Value'):
                w2 = tf.get_variable('w2', [nl_1, 1], initializer=w_initializer, collections=name)
                b2 = tf.get_variable('b2', [1, 1], initializer=b_initializer, collections=name)
                self.V = tf.matmul(l1, w2) + b2

            with tf.variable_scope('Advantage'):
                w2 = tf.get_variable('w2', [nl_1, self.OUTPUT_NUM], initializer=w_initializer, collections=name)
                b2 = tf.get_variable('b2', [1, self.OUTPUT_NUM], initializer=b_initializer, collections=name)
                self.A = tf.matmul(l1, w2) + b2
            with tf.variable_scope('Q'):
                out = self.V + (self.A - tf.reduce_mean(self.A, axis=1, keep_dims=True))

            return out

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

            self.q_eval_output = _dueling_builder(self.q_eval_input, q_name, neuro_layer_1, w_init, b_init)
            self.q_predict = tf.argmax(self.q_eval_output, 1)

        with tf.variable_scope('loss'):
            self.loss = tf.reduce_mean(tf.squared_difference(self.q_eval_target, self.q_eval_output))

        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]

            self.q_target_output = _dueling_builder(self.q_target_input, t_name, neuro_layer_1, w_init, b_init)

 

完整代码

优先采样:

import numpy as np
import random


class SumTree:
    write = 0

    def __init__(self, capacity):
        # 容量。
        self.capacity = capacity
        self.tree = np.zeros(2 * capacity - 1)
        self.data = np.zeros(capacity, dtype=object)

    def _propagate(self, idx, change):
        # 整数除法。
        parent = (idx - 1) // 2

        self.tree[parent] += change

        if parent != 0:
            self._propagate(parent, change)

    def _retrieve(self, idx, s):
        """
        示例:
          样本数(sample num):4 个,则 Tree 的 size 是:4 X 2 - 1 = 7。
          其中,size 的后 sample num 个是单个样本的优先级,其它的是父级,即:父级的优先级=SUM(子级的优先级)。
          如果 data = ["test1", "test2", "test3", "test4"],它们单个的优先级对应的就是下图中的“3, 4, 5, 6”。
        Tree structure and array storage:

        Tree index:
             18         -> storing priority sum
            / \
          7    11
         / \   / \
        3   4 5   6    -> storing priority for transitions

        Array type for storing:
        [18,7,11,3,4,5,6]
        """
        left = 2 * idx + 1
        right = left + 1

        if left >= len(self.tree):
            return idx

        if s <= self.tree[left]:
            return self._retrieve(left, s)
        else:
            return self._retrieve(right, s - self.tree[left])

    def total(self):
        return self.tree[0]

    def add(self, p, data):
        idx = self.write + self.capacity - 1

        self.data[self.write] = data
        self.update(idx, p)

        self.write += 1
        if self.write >= self.capacity:
            self.write = 0

    def update(self, idx, p):
        change = p - self.tree[idx]

        self.tree[idx] = p
        self._propagate(idx, change)

    def get(self, s):
        idx = self._retrieve(0, s)
        data_index = idx - self.capacity + 1

        return idx, self.tree[idx], self.data[data_index]


class Memory:
    e = 0.01
    a = 0.6

    def __init__(self, capacity):
        self.tree = SumTree(capacity)

    def _get_priority(self, error):
        return (error + self.e) ** self.a

    def add(self, error, sample):
        p = self._get_priority(error)
        self.tree.add(p, sample)

    def sample(self, n):
        batch = []

        # 计算样本抽取的区间。
        segment = self.tree.total() / n

        for i in range(n):
            # 第 i 个样本抽取区间的开始序号。
            a = segment * i
            # 第 i 个样本抽取区间的结束序号。
            b = segment * (i + 1)

            # 在(a, b)的区间内随机选数。
            s = random.uniform(a, b)
            # 根据 s 来抽取,并将数据(数据序号,优先级,数据)返回。
            (idx, p, data) = self.tree.get(s)
            # 将样本增加到集合里。
            batch.append((idx, data))

        return batch

    def update(self, idx, error):
        p = self._get_priority(error)
        self.tree.update(idx, p)

if __name__ == "__main__":
    memory = Memory(4)
    memory.add(1, "test1")
    print(memory.tree.data)
    print(memory.tree.tree)
    memory.add(1, "test2")
    print(memory.tree.data)
    print(memory.tree.tree)
    memory.add(2, "test3")
    memory.add(3, "test4")
    samples, sample_index, i_s_weight = memory.sample(2)
    print("samples:", samples)
    print("sample_index:", sample_index)
    print("Importance-Sampling Weight", i_s_weight)

神经网络:

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

    V = None
    A = None

    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())
        else:
            self.session = session

    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_eval_target = tf.placeholder(shape=[None, self.OUTPUT_NUM], dtype=tf.float32, name="q_target")

        def _dueling_builder(input_data, name, nl_1, w_initializer, b_initializer):
            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.INPUT_NUM, nl_1], initializer=w_initializer, collections=name)
                b1 = tf.get_variable('b1', [1, nl_1], initializer=b_initializer, collections=name)
                l1 = tf.nn.relu(tf.matmul(input_data, w1) + b1)

            with tf.variable_scope('Value'):
                w2 = tf.get_variable('w2', [nl_1, 1], initializer=w_initializer, collections=name)
                b2 = tf.get_variable('b2', [1, 1], initializer=b_initializer, collections=name)
                self.V = tf.matmul(l1, w2) + b2

            with tf.variable_scope('Advantage'):
                w2 = tf.get_variable('w2', [nl_1, self.OUTPUT_NUM], initializer=w_initializer, collections=name)
                b2 = tf.get_variable('b2', [1, self.OUTPUT_NUM], initializer=b_initializer, collections=name)
                self.A = tf.matmul(l1, w2) + b2
            with tf.variable_scope('Q'):
                out = self.V + (self.A - tf.reduce_mean(self.A, axis=1, keep_dims=True))

            return out

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

            self.q_eval_output = _dueling_builder(self.q_eval_input, q_name, neuro_layer_1, w_init, b_init)
            self.q_predict = tf.argmax(self.q_eval_output, 1)

        with tf.variable_scope('loss'):
            self.loss = tf.reduce_mean(tf.squared_difference(self.q_eval_target, self.q_eval_output))

        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]

            self.q_target_output = _dueling_builder(self.q_target_input, t_name, neuro_layer_1, w_init, b_init)

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

主逻辑功能:

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


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, train_num=2000):
        # 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

        self.replay_memory_store = Memory(self.memory_size)

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

        self.TRAIN_NUM = train_num

        # 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=1000,
                                    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)

        # 输出图表。
        self.r_list = [0]

    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]

            # 保存数据(当前状态, 当前执行的动作, 当前动作的得分,下一个状态,是否结束)。
            memory_data = (current_state, current_action, current_reward, next_state, done)

            x, y, errors = self._get_targets([(0, memory_data)])
            self.replay_memory_store.add(errors[0], memory_data)

            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 = self.replay_memory_store.sample(batch)

        x, y, errors = self._get_targets(minibatch)

        # update errors
        for i in range(len(minibatch)):
            idx = minibatch[i][0]
            self.replay_memory_store.update(idx, errors[i])

        self.network.train(x, y)

        self.learn_step_counter += 1

    def _get_targets(self, batch):
        # (当前状态,当前动作,当前得分,下一个状态) = (s, a, r, s_)。
        current_states = np.vstack([o[1][0] for o in batch])
        current_actions = np.vstack([o[1][1] for o in batch])
        current_rewards = np.vstack([o[1][2] for o in batch])
        next_states = np.vstack([o[1][3] for o in batch])

        # 当前状态在 Q 网络的得分。
        p = self.network.get_q(current_states)
        # 下一状态在 Q 网络的得分。
        p_ = self.network.get_q(next_states)
        # 下一状态在 Target 网络的得分。
        p_target = self.network.get_next_q(next_states)

        x = np.zeros((len(batch), self.STATE_NUM))
        y = np.zeros((len(batch), self.ACTION_NUM))
        errors = np.zeros(len(batch))

        for i in range(len(batch)):
            s = current_states[i]
            a = current_actions[i]
            r = current_rewards[i][0]
            s_ = next_states[i]

            a_index = np.argmax(a)

            # 获得第 i 个样本当前状态的所有动作的 Q 值表。
            target_q = p[i]
            # 获得第 i 个样本当前动作的 Q 得分。
            current_q = target_q[a_index]

            max_q = p_target[i][np.argmax(p_[i])]

            if r <= -1 or s_ is None:
                target_q[a_index] = r
            else:
                target_q[a_index] = r + self.gamma * max_q

            x[i] = s
            y[i] = target_q
            errors[i] = abs(current_q - target_q[a_index])

        return x, y, errors

    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)

            test_reward = reward / 10
            self.r_list.append(test_reward + self.r_list[-1])

            # 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 > self.TRAIN_NUM:
                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", "#############################")

    def show_plt(self):
        import matplotlib.pyplot as plt
        plt.plot(np.array(self.r_list), c='r', label='dueling')
        # plt.plot(np.array(q_double), c='b', label='double')
        plt.legend(loc='best')
        plt.ylabel('accumulated reward')
        plt.xlabel('training steps')
        plt.grid()
        plt.show()

if __name__ == "__main__":
    agent = Agent(train_num=20000)
    agent.pay()
    agent.show_plt()

 

转载于:https://www.cnblogs.com/cjnmy36723/p/7063213.html

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