DQN 处理 CartPole 问题——使用强化学习，本质上是训练MLP，预测每一个动作的得分

代码：

# -*- coding: utf-8 -*-

import random

import gym

import numpy as np

from collections import deque

from keras.models import Sequential

from keras.layers import Dense

from keras.optimizers import Adam

from keras.utils.vis_utils import plot_model

EPISODES = 1000

class DQNAgent:

    def __init__(self, state_size, action_size):

        self.state_size = state_size

        self.action_size = action_size

        self.memory = deque(maxlen=2000)

        self.gamma = 0.95    # discount rate

        #self.epsilon = 1.0  # exploration rate

        self.epsilon = 0.4  # exploration rate

        self.epsilon_min = 0.01

        self.epsilon_decay = 0.995

        self.learning_rate = 0.001

        self.model = self._build_model()

        #可视化MLP结构

        plot_model(self.model, to_file='dqn-cartpole-v0-mlp.png', show_shapes=False)

    def _build_model(self):

        # Neural Net for Deep-Q learning Model

        model = Sequential()

        model.add(Dense(24, input_dim=self.state_size, activation='relu'))

        model.add(Dense(24, activation='relu'))

        model.add(Dense(self.action_size, activation='linear'))

        model.compile(loss='mse',

                      optimizer=Adam(lr=self.learning_rate))

        return model

    def remember(self, state, action, reward, next_state, done):

        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):

        if np.random.rand() <= self.epsilon:

            return random.randrange(self.action_size)

        act_values = self.model.predict(state)

        #print("act_values:")

        #print(act_values)

        return np.argmax(act_values[0])  # returns action

    def replay(self, batch_size):

        minibatch = random.sample(self.memory, batch_size)

        for state, action, reward, next_state, done in minibatch:

            target = reward

            if not done:

                target = (reward + self.gamma *

                          np.amax(self.model.predict(next_state)[0]))

            target_f = self.model.predict(state)

            target_f[0][action] = target

            self.model.fit(state, target_f, epochs=1, verbose=0)

        #if self.epsilon > self.epsilon_min:

        #    self.epsilon *= self.epsilon_decay

    def load(self, name):

        self.model.load_weights(name)

    def save(self, name):

        self.model.save_weights(name)

if __name__ == "__main__":

    env = gym.make('CartPole-v0')

    state_size = env.observation_space.shape[0]

    action_size = env.action_space.n

    #print(state_size)

    #print(action_size)

    agent = DQNAgent(state_size, action_size)

    done = False

    batch_size = 32

    avg=0

    for e in range(EPISODES):

        state = env.reset()

        state = np.reshape(state, [1, state_size])

        for time in range(500):

            env.render()

            action = agent.act(state)

            next_state, reward, done, _ = env.step(action)

            reward = reward if not done else -10

            next_state = np.reshape(next_state, [1, state_size])

            agent.remember(state, action, reward, next_state, done)

            state = next_state

            if done:

                print("episode: {}/{}, score: {}, e: {:.2}"

                      .format(e, EPISODES, time, agent.epsilon))

                avg+=time

                break

        if len(agent.memory) > batch_size:

            agent.replay(batch_size)

    print("Avg score:{}".format(avg/1000))

基本思路：

让他自己训练玩这个游戏（每次应该左右移动的距离），基本思路就是：

本质上就是使用MLP训练（动作，得分）

这个得分是坚持时间的长短，如果时间长得分就高。

但是我感觉这个gym自己做了很多事情，比如度量奖励分数，action描述等。待进一步挖掘！