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()
