【Python+Tensorflow】Deep Q Network (DQN) 迷宫示例代码整理

Overview

本文代码整理自Morvan Zhou(莫烦Python [1])的机器学习相关教程 - 强化学习 - DQN部分 [2]

Deep Q Network简称DQN，结合了Q learning和Neural networks的优势。如果我们使用tabular Q learning，对于每一个state，action我们都需要存放在一张q_table的表中。而在现实生活中，情况可就比迷宫的状况复杂多了，我们有千千万万个state，如果将这千万个state的值都放在表中，受限于我们计算机硬件，这样从表中获取数据，更新数据是没有效率的，这就是 DQN 产生的原因了。我们可以使用神经网络来估算这个state的值，这样就不需要一张表了。

算法流程：

网络结构：
为了使用Tensorflow来实现DQN，比较推荐的方式是搭建两个神经网络：target_net用于预测q_target值，不会及时更新参数；eval_net用于预测q_eval，这个神经网络拥有最新的神经网络参数。不过这两个神经网络结构是完全一样的，只是里面的参数不一样。

代码中还提供了一种修改版DQN：
将q_target的计算也加在了Tensorflow的graph里面。不过实测的时候效率会比之前的方法慢10%左右，原因我想应该是使用了one hot，而且貌似丢失了一点精度，因为在q_target转换的时候，之前是用numpy在做，而且是以float64的进度计算的，但是如果将q_target的计算全部挪进tf中，精度都是float32。不过这种结构还是有好处的，作为学习样本的话，计算结构全部在tensorboard上，代码结构也更好理解。

详细代码另可参考：GitHub [3]

Code

本教程代码主要基于一个简单的迷宫环境，重点在实现 DQN 算法
迷宫如下图所示，红色表示explorer，黄色表示paradise，黑色表示hell，白色表示ground（可自定义）

本算法主要模拟的是learn to move explorer to paradise的过程
之后我们可以再拿着做好的DQN算法去尝试其他更有意思的环境

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#!/usr/bin/env python#-*- coding: utf-8 -*-############################################################ Title : Deep Q Network (DQN)# Author : Alex Pan# Info : Forked from Morvan Zhou (using tensorflow r1.2 gym 0.7.3)## Tensorboard picture about this DQN structure:# https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/#modification###########################################################from __future__ import print_function, divisionimport sysimport timeimport numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltif sys.version_info.major == 2:    import Tkinter as tkelse:    import tkinter as tkimport ipdb#################################### Initialization ###################################### Set Random Seednp.random.seed(1)tf.set_random_seed(1)#################################### Initialization ######################################################################## Global Parameters #################################### Data TypeuintType = np.uint8 # cv2.imshow() ONLY support uint8[0, 255] & double[0, 1]floatType = np.float32################################### Global Parameters ########################################################################### Classes ######################################### Deep Q Network off-policyclass DeepQNetwork(object):    def __init__(self, n_actions, n_features,            learning_rate = 0.01,            reward_decay = 0.9,            e_greedy = 0.9,            e_greedy_increment = None,            replace_target_iter = 300,            memory_size = 500,            batch_size = 32,            output_graph = False,    ):        self.n_actions = n_actions        self.n_features = n_features        self.lr = learning_rate        self.gamma = reward_decay        self.replace_target_iter = replace_target_iter        self.memory_size = memory_size        self.batch_size = batch_size        self.epsilon_max = e_greedy        self.epsilon_increment = e_greedy_increment        self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max        # consist of [target_net, evaluate_net]        self.__build_net()        # Get Parameters to be Updated        t_params = tf.get_collection('target_net_params')        e_params = tf.get_collection('eval_net_params')        self.replace_target_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]        # initialize zero memory [s, a, r, s_]        self.memory = np.zeros((self.memory_size, n_features * 2 + 2))        # total learning step & Cost Histogram        self.learn_step_counter = 0        self.cost_his = []        # Start Session        self.sess = tf.Session()        if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: # tensorflow version < 0.12            self.sess.run(tf.initialize_all_variables())            if output_graph:                # $ tensorboard --logdir=logs                writer = tf.train.SummaryWriter('logs/', self.sess.graph)        else: # tensorflow version >= 0.12            self.sess.run(tf.global_variables_initializer())            if output_graph:                # $ tensorboard --logdir=logs                writer = tf.summary.FileWriter('logs/', self.sess.graph)    def __build_net(self):        n_h1 = 10 # Hidden Layer        w_initializer, b_initializer = tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers        # ------------------ build evaluate_net ------------------        self.s = tf.placeholder(tf.float32, [None, self.n_features], name = 's') # input        self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name = 'Q_target') # for calculating loss        with tf.variable_scope('eval_net'):            # c_names(collections_names) are the collections to store variables            c_names = ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES]            # first layer. collections is used later when assign to target net            with tf.variable_scope('l1'):                w1 = tf.get_variable('w1', [self.n_features, n_h1], initializer = w_initializer, collections = c_names)                b1 = tf.get_variable('b1', [1, n_h1], 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 net            with tf.variable_scope('l2'):                w2 = tf.get_variable('w2', [n_h1, 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        # ------------------ loss & optimizer ------------------        with 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_') # input        with tf.variable_scope('target_net'):            # c_names(collections_names) are the collections to store variables            c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]            # first layer. collections is used later when assign to target net            with tf.variable_scope('l1'):                w1 = tf.get_variable('w1', [self.n_features, n_h1], initializer = w_initializer, collections = c_names)                b1 = tf.get_variable('b1', [1, n_h1], 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 net            with tf.variable_scope('l2'):                w2 = tf.get_variable('w2', [n_h1, 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    def choose_action(self, observation):        # to have batch dimension when feed into tf placeholder        observation = observation[np.newaxis, :]        if np.random.uniform() < self.epsilon:            # forward feed the observation and get q value for every actions            actions_value = self.sess.run(self.q_eval, feed_dict = {self.s: observation})            action = np.argmax(actions_value) # Returns the indices of the maximum values along an axis        else:            action = np.random.randint(0, self.n_actions)        return action    def store_transition(self, s, a, r, s_):        if not hasattr(self, 'memory_counter'):            self.memory_counter = 0        # replace the old memory with new memory        transition = np.hstack((s, [a, r], s_))        index = self.memory_counter % self.memory_size        self.memory[index, :] = transition        self.memory_counter += 1    def learn(self):        # check to replace target parameters        if self.learn_step_counter % self.replace_target_iter == 0:            self.sess.run(self.replace_target_op)            print('\n' + 'target_params_replaced' + '\n')        self.learn_step_counter += 1        # sample batch memory from all memory        if self.memory_counter > self.memory_size:            sample_index = np.random.choice(self.memory_size, size = self.batch_size) # Generates a random sample from a given 1-D array        else:            sample_index = np.random.choice(self.memory_counter, size = self.batch_size)        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                }            )        # change q_target w.r.t q_eval's action        q_target = q_eval.copy()        batch_index = np.arange(self.batch_size, dtype = np.int32)        action_index = batch_memory[:, self.n_features].astype(int) # Action Index of all batch memory, length equal to batch_index        reward = batch_memory[:, self.n_features + 1]        # Update Q Matrix        q_target[batch_index, action_index] = reward + self.gamma * np.max(q_next, axis = 1)        '''        For example in this batch we have 2 samples and 3 actions:        q_eval =        [[1, 2, 3],         [4, 5, 6]]        q_target = q_eval =        [[1, 2, 3],         [4, 5, 6]]        Then change q_target with the real q_target value w.r.t the q_eval's action.        For example in:            sample 0, I took action 0, and the max q_target value is -1;            sample 1, I took action 2, and the max q_target value is -2:        q_target =        [[-1, 2, 3],         [4, 5, -2]]        So the (q_target - q_eval) becomes:        [[(-1)-(1), 0, 0],         [0, 0, (-2)-(6)]]        We then backpropagate this error w.r.t the corresponding action to network,        leave other action as error=0 cause we didn't choose it.        '''        # 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 epsilon        self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max    def plot_cost(self):        plt.plot(np.arange(len(self.cost_his)), self.cost_his)        plt.ylabel('Cost')        plt.xlabel('training steps')        plt.show()# Deep Q Network off-policyclass DeepQNetwork_modified(object):    def __init__(self, n_actions, n_features,        learning_rate = 0.01,        reward_decay = 0.9,        e_greedy = 0.9,        e_greedy_increment = None,        replace_target_iter = 300,        batch_size = 32,        memory_size = 500,        output_graph = False,    ):        self.n_actions = n_actions        self.n_features = n_features        self.lr = learning_rate        self.gamma = reward_decay        self.replace_target_iter = replace_target_iter        self.memory_size = memory_size        self.batch_size = batch_size        self.epsilon_max = e_greedy        self.epsilon_increment = e_greedy_increment        self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max        # consist of [target_net, evaluate_net]        self.__build_net()        # Get Parameters to be Updated        t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope = 'target_net')        e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope = 'eval_net')        self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]        # initialize zero memory [s, a, r, s_]        self.memory = np.zeros((self.memory_size, n_features * 2 + 2))        # total learning step & Cost Histogram        self.learn_step_counter = 0        self.cost_his = []        # Start Session        self.sess = tf.Session()        if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: # tensorflow version < 0.12            self.sess.run(tf.initialize_all_variables())            if output_graph:                # $ tensorboard --logdir=logs                writer = tf.train.SummaryWriter('logs/', self.sess.graph)        else: # tensorflow version >= 0.12            self.sess.run(tf.global_variables_initializer())            if output_graph:                # $ tensorboard --logdir=logs                writer = tf.summary.FileWriter('logs/', self.sess.graph)    def __build_net(self):        n_h1 = 20 # Hidden Layer        w_initializer, b_initializer = tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1) # config of layers        # ------------------ all inputs ------------------------        self.s = tf.placeholder(tf.float32, [None, self.n_features], name = 's') # input State        self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name = 's_') # input Next State        self.a = tf.placeholder(tf.int32, [None,], name = 'a') # input Action        self.r = tf.placeholder(tf.float32, [None,], name = 'r') # input Reward        # ------------------ build evaluate_net ------------------        with tf.variable_scope('eval_net'):            l1 = tf.layers.dense(self.s, n_h1, tf.nn.relu, kernel_initializer = w_initializer, bias_initializer = b_initializer)            self.q_eval = tf.layers.dense(l1, self.n_actions, kernel_initializer = w_initializer, bias_initializer = b_initializer)        with tf.variable_scope('q_eval'):            a_indices = tf.stack([tf.range(tf.shape(self.a)[0], dtype = tf.int32), self.a], axis = 1)            self.q_eval_wrt_a = tf.gather_nd(params = self.q_eval, indices = a_indices) # shape=(None,)        # ------------------ build target_net ------------------        with tf.variable_scope('target_net'):            l1 = tf.layers.dense(self.s_, n_h1, tf.nn.relu, kernel_initializer = w_initializer, bias_initializer = b_initializer)            self.q_next = tf.layers.dense(l1, self.n_actions, kernel_initializer = w_initializer, bias_initializer = b_initializer)        with tf.variable_scope('q_target'):            q_target = self.r + self.gamma * tf.reduce_max(self.q_next, axis = 1, name = 'Qmax_s_') # shape=(None,)            self.q_target = tf.stop_gradient(q_target)        # ------------------ loss & optimizer ------------------        with tf.variable_scope('loss'):            self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval_wrt_a, name = 'TD_error'))        with tf.variable_scope('train'):            self.train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)    def choose_action(self, observation):        # to have batch dimension when feed into tf placeholder        observation = observation[np.newaxis, :]        if np.random.uniform() < self.epsilon:            # forward feed the observation and get q value for every actions            actions_value = self.sess.run(self.q_eval, feed_dict = {self.s: observation})            action = np.argmax(actions_value) # Returns the indices of the maximum values along an axis        else:            action = np.random.randint(0, self.n_actions)        return action    def store_transition(self, s, a, r, s_):        if not hasattr(self, 'memory_counter'):            self.memory_counter = 0        # replace the old memory with new memory        transition = np.hstack((s, [a, r], s_))        index = self.memory_counter % self.memory_size        self.memory[index, :] = transition        self.memory_counter += 1    def learn(self):        # check to replace target parameters        if self.learn_step_counter % self.replace_target_iter == 0:            self.sess.run(self.target_replace_op)            print('\n' + 'target_params_replaced' + '\n')        self.learn_step_counter += 1        # sample batch memory from all memory        if self.memory_counter > self.memory_size:            sample_index = np.random.choice(self.memory_size, size = self.batch_size) # Generates a random sample from a given 1-D array        else:            sample_index = np.random.choice(self.memory_counter, size = self.batch_size)        batch_memory = self.memory[sample_index, :]        _, cost = self.sess.run([self.train_op, self.loss],            feed_dict = {                self.s: batch_memory[:, :self.n_features],                self.a: batch_memory[:, self.n_features],                self.r: batch_memory[:, self.n_features + 1],                self.s_: batch_memory[:, -self.n_features:],                }            )        self.cost_his.append(cost)        # increasing epsilon        self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max    def plot_cost(self):        plt.plot(np.arange(len(self.cost_his)), self.cost_his)        plt.ylabel('Cost')        plt.xlabel('training steps')        plt.show()''' Reinforcement learning maze exampleRed rectangle:         explorerBlack rectangles:      hells       [reward = -1]Yellow bin circle:     paradise    [reward = +1]All other states:      ground      [reward = 0]'''class Maze(tk.Tk, object): # Forked from Tkinter    def __init__(self, unit_pixels = 40, grid_height = 4, grid_width = 4):        super(Maze, self).__init__() # Initialize Upper Object        self.UNIT = unit_pixels        self.MAZE_H = grid_height        self.MAZE_W = grid_width        self.action_space = ['u', 'd', 'l', 'r'] # up, down, left, right        self.n_actions = len(self.action_space) # 4        self.n_features = 2 # 2D map        self.title('Maze')        self.geometry('{0}x{1}'.format(self.MAZE_W * self.UNIT, self.MAZE_H * self.UNIT))        self.__build_maze()    def __build_maze(self):        # Create Canvas Widget to Display        self.canvas = tk.Canvas(self, bg = 'white', height = self.MAZE_H * self.UNIT, width = self.MAZE_W * self.UNIT)        # Create line with coordinates x1,y1,...,xn,yn (Grids)        for col in xrange(0, self.MAZE_W):            x0, y0 = col * self.UNIT, 0            x1, y1 = col * self.UNIT, self.MAZE_H * self.UNIT            self.canvas.create_line(x0, y0, x1, y1)        for row in xrange(0, self.MAZE_H):            x0, y0 = 0, row * self.UNIT            x1, y1 = self.MAZE_H * self.UNIT, row * self.UNIT            self.canvas.create_line(x0, y0, x1, y1)        # create origin, center of (0, 0)        origin = np.array([self.UNIT / 2, self.UNIT / 2])        # Hell 1 (black rect)        hell1_center = origin + np.array([self.UNIT * 2, self.UNIT]) # center of (2, 1)        x0, y0 = hell1_center[0] - 15, hell1_center[1] - 15        x1, y1 = hell1_center[0] + 15, hell1_center[1] + 15        self.hell1 = self.canvas.create_rectangle(x0, y0, x1, y1, fill = 'black') # Create rectangle with coordinates x1,y1,x2,y2        # Hell 2 (black rect)        hell2_center = origin + np.array([self.UNIT, self.UNIT * 2]) # center of (1, 2)        x0, y0 = hell2_center[0] - 15, hell2_center[1] - 15        x1, y1 = hell2_center[0] + 15, hell2_center[1] + 15        self.hell2 = self.canvas.create_rectangle(x0, y0, x1, y1, fill = 'black')        # Paradise (yellow oval)        oval_center = origin + self.UNIT * 2 # center of (2, 2)        x0, y0 = oval_center[0] - 15, oval_center[1] - 15        x1, y1 = oval_center[0] + 15, oval_center[1] + 15        self.paradise = self.canvas.create_oval(x0, y0, x1, y1, fill = 'yellow') # Create oval with coordinates x1,y1,x2,y2        # Explorer (red rect)        x0, y0 = origin[0] - 15, origin[1] - 15        x1, y1 = origin[0] + 15, origin[1] + 15        self.explorer = self.canvas.create_rectangle(x0, y0, x1, y1, fill = 'red')        # Pack All        self.canvas.pack()    def move(self, action):        coords = self.canvas.coords(self.explorer)        # Update Action with Boarder Constrains        base_action = np.array([0, 0])        if action == 0: # up            if coords[1] > self.UNIT:                base_action[1] -= self.UNIT        elif action == 1: # down            if coords[1] < (self.MAZE_H - 1) * self.UNIT:                base_action[1] += self.UNIT        elif action == 2: # right            if coords[0] < (self.MAZE_W - 1) * self.UNIT:                base_action[0] += self.UNIT        elif action == 3: # left            if coords[0] > self.UNIT:                base_action[0] -= self.UNIT        # Move Explorer        self.canvas.move(self.explorer, base_action[0], base_action[1]) # Move an item TAGorID given in ARGs        next_coords = self.canvas.coords(self.explorer) # next position        # reward function        if next_coords in [self.canvas.coords(self.paradise)]:            reward = 1            done = True        elif next_coords in [self.canvas.coords(self.hell1), self.canvas.coords(self.hell2)]:            reward = -1            done = True        else:            reward = 0            done = False        # return observation        observation_ = (np.array(next_coords[:2]) - np.array(self.canvas.coords(self.paradise)[:2])) / (self.MAZE_H * self.UNIT)        return observation_, reward, done    def reset(self):        # Enter event loop until all pending events have been processed by Tcl        self.update()        time.sleep(0.1)        # Reset Explorer        self.canvas.delete(self.explorer) # Delete items identified by all tag or ids contained in ARGS        origin = np.array([self.UNIT / 2, self.UNIT / 2])        x0, y0 = origin[0] - 15, origin[1] - 15        x1, y1 = origin[0] + 15, origin[1] + 15        self.explorer = self.canvas.create_rectangle(x0, y0, x1, y1, fill = 'red')        # return observation        return (np.asarray(self.canvas.coords(self.explorer)[:2]) - np.asarray(self.canvas.coords(self.paradise)[:2])) / (self.MAZE_H * self.UNIT)    def render(self):        time.sleep(0.01)        self.update()######################################## Classes ############################################################################### Functions #######################################def run_maze(M, DQN):    step = 0    for episode in xrange(300):        # initial observation        observation = M.reset()        while True:            step += 1            # fresh M            M.render()            # DQN choose action based on observation            action = DQN.choose_action(observation)            # DQN take action and get next observation and reward            observation_, reward, done = M.move(action)            DQN.store_transition(observation, action, reward, observation_)            if step > 200 and step % 5 == 0:                DQN.learn()            # update observation            observation = observation_            # break while loop when end of this episode            if done:                break    # end of game    print('Game Over.')    M.destroy()####################################### Functions ################################################################################ Mains ########################################## MAIN Function of DQN-Mazedef main():    # Initialize All    M = Maze()    DQN = DeepQNetwork(M.n_actions, M.n_features,        learning_rate = 0.01,        reward_decay = 0.9,        e_greedy = 0.9,        replace_target_iter = 200,        memory_size = 2000,        output_graph = False,)    # Run Maze    arg = (M, DQN)    M.after(100, run_maze, *arg) # Call function once after given time    M.mainloop() # Call the mainloop of Tk    # Plot    DQN.plot_cost()######################################### Mains ###################################################################if __name__ == '__main__':    main()

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References

[1] 教程一览 - 莫烦Python
[2] DQN算法更新 (Tensorflow) - 莫烦Python
[3] GitHub - MorvanZhou/tutorials

希望能够对大家有所帮助~