机器学习---Deep Q-Network (DQN)

1. RL_brain

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()
        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

        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 +=