莫烦python强化学习系列-DQN学习(代码)_n_features

 
import numpy as np
import pandas as pd
import tensorflow as tf
 
np.random.seed(1)
tf.set_random_seed(1)
 
 
# Deep Q Network off-policy
class DeepQNetwork:
    def __init__(
            self,
            n_actions,
            n_features,
            learning_rate=0.01,
            reward_decay=0.9,
            e_greedy=0.9,
            replace_target_iter=300,
            memory_size=500,
            batch_size=32,
            e_greedy_increment=None,
            output_graph=False,
    ):
        self.n_actions = n_actions 
        self.n_features = n_features
        self.lr = learning_rate
        self.gamma = reward_decay
        self.epsilon_max = e_greedy
        self.replace_target_iter = replace_target_iter
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.epsilon_increment = e_greedy_increment
        self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max
 
        # total learning step
        self.learn_step_counter = 0
 
        # initialize zero memory [s, a, r, s_]
        self.memory = np.zeros((self.memory_size, n_features * 2 + 2))
 
        # consist of [target_net, evaluate_net]
        self._build_net()
        #tf.get_collection(key, scope=None) 
        #用来获取一个名称是‘key’的集合中的所有元素，返回的是一个列表
        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)]
 
        self.sess = tf.Session()
 
        if output_graph:
            # $ tensorboard --logdir=logs
            # tf.train.SummaryWriter soon be deprecated, use following
            tf.summary.FileWriter("logs/", self.sess.graph)
 
        self.sess.run(tf.global_variables_initializer())
        self.cost_his = []
 
    def _build_net(self):
        # ------------------ 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, n_l1, w_initializer, b_initializer = \
                ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 10, \
                tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1)  # config of layers
 
            # 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_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)
 
            # second layer. collections is used later when assign to target net
            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    #[batch_size,self.n_action]
 
        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_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)
 
            # second layer. collections is used later when assign to target net
            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
 
    def store_transition(self, s, a, r, s_):
        if not hasattr(self, 'memory_counter'):
            self.memory_counter = 0
 
        transition = np.hstack((s, [a, r], s_))
 
        # replace the old memory with new memory
        index = self.memory_counter % self.memory_size
        self.memory[index, :] = transition
 
        self.memory_counter += 1
 
    def choose_action(self, observation):
        # to have batch dimension when feed into tf placeholder
        observation = observation[np.newaxis, :]   #shape=(1,n_features)
 
        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)#未加axis＝,返回一个索引数值
        else:
            action = np.random.randint(0, self.n_actions)
        return action
 
    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('\ntarget_params_replaced\n')
 
        # 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)
        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={
                #[s, a, r, s_]
                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)
        eval_act_index = batch_memory[:, self.n_features].astype(int)  #action   astype(int) 转换数组的数据类型
        reward = batch_memory[:, self.n_features + 1]                  #reward                       
        q_target[batch_index, eval_act_index] = reward + self.gamma * np.max(q_next, axis=1)
        """
        For example in this batch I 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
        self.learn_step_counter += 1
    def plot_cost(self):
        import matplotlib.pyplot as plt
        plt.plot(np.arange(len(self.cost_his)), self.cost_his)
        plt.ylabel('Cost')
        plt.xlabel('training steps')
        plt.show()

from maze_env import Maze
from RL_brain import DeepQNetwork
 
 
def run_maze():
    step = 0
    for episode in range(300):
        # initial observation
        observation = env.reset()
 
        while True:
            # fresh env
            env.render()
 
            # RL choose action based on observation
            action = RL.choose_action(observation)
 
            # RL take action and get next observation and reward
            observation_, reward, done = env.step(action)
 
            RL.store_transition(observation, action, reward, observation_)
 
            if (step > 200) and (step % 5 == 0):
                RL.learn()
 
            # swap observation
            observation = observation_
 
            # break while loop when end of this episode
            if done:
                break
            step += 1
 
    # end of game
    print('game over')
    env.destroy()
 
 
if __name__ == "__main__":
    # maze game
    env = Maze()
    RL = DeepQNetwork(env.n_actions, env.n_features,
                      learning_rate=0.01,
                      reward_decay=0.9,
                      e_greedy=0.9,
                      replace_target_iter=200,
                      memory_size=2000,
                      # output_graph=True
                      )
    env.after(100, run_maze)
    env.mainloop()
    RL.plot_cost()