DQN算法 

  1 # coding=utf-8
  2 
  3 import sys
  4 import os
  5 import gym
  6 import pylab
  7 import random
  8 import numpy as np
  9 from collections import deque
 10 from keras.layers import Dense
 11 from keras.optimizers import Adam
 12 from keras.models import Sequential
 13 from keras.callbacks import Callback
 14 import matplotlib.pyplot as plt
 15 
 16 class LossHistory(Callback):
 17     def on_train_begin(self, logs={}):
 18         self.losses = []
 19 
 20     def on_batch_end(self, batch, logs={}):
 21         self.losses.append(logs.get('loss'))
 22 
 23 class DQNAgent:
 24     def __init__(self, state_size, action_size,
 25                  render=False, load_model=False,
 26                  gamma=0.99, learning_rate=0.001,
 27                  epsilon=1.0, epsilon_decay=0.999,
 28                  epsilon_min=0.01, batch_size=64,
 29                  train_start=100, memory_size=2000,
 30                 ):
 31         # env 的状态空间的设置
 32         self.state_size = state_size
 33         self.action_size = action_size
 34 
 35         # render表示是否打开gym下的动画展示,打开的话运行速度会大幅减慢
 36         self.render = render
 37         # load_model=True表示从文件中加载model
 38         self.load_model = load_model
 39 
 40         # 接下来的都是DQN的超参
 41         self.gamma = gamma
 42         self.learning_rate = learning_rate
 43         self.epsilon = epsilon
 44         self.epsilon_decay = epsilon_decay  # e-贪心的e值随着步骤不断减小的比例
 45         self.epsilon_min = epsilon_min  # e-贪心的e值减小到一个阈值不再减小
 46 
 47         self.train_start = train_start
 48         self.batch_size = batch_size
 49 
 50         # 记忆数据存储模块
 51         self.memory = deque(maxlen=memory_size)
 52 
 53         # 初始化模型
 54         self.model = self.build_model()
 55 
 56         # 记录损失值
 57         self.history = LossHistory()
 58         self.losses_list = []
 59 
 60     def build_model(self, units=128):
 61         model = Sequential()
 62         model.add(Dense(units, input_dim=self.state_size,
 63                         activation='sigmoid', kernel_initializer='he_uniform'))
 64         model.add(Dense(units, activation='sigmoid',
 65                         kernel_initializer='he_uniform'))
 66         model.add(Dense(self.action_size, activation='linear',
 67                         kernel_initializer='he_uniform'))
 68         model.summary()
 69 
 70         model.compile(loss='mean_squared_error', optimizer=Adam(lr=self.learning_rate))
 71         return model
 72 
 73     def choose_action(self, state):
 74         if np.random.rand() <= self.epsilon:
 75             return random.randrange(self.action_size)
 76         else:
 77             q_value = self.model.predict(state)
 78             return np.argmax(q_value[0])
 79 
 80     def add_memory(self, state, action, reward, done, next_state):
 81         self.memory.append((state, action, reward, done, next_state))
 82         if self.epsilon > self.epsilon_min:
 83             self.epsilon *= self.epsilon_decay
 84 
 85     def train_model(self):
 86         if len(self.memory) < self.train_start:
 87             return
 88         batch_size = min(self.batch_size, len(self.memory))
 89         min_batch = random.sample(self.memory, batch_size)
 90 
 91         update_input = np.zeros((batch_size, self.state_size))
 92         update_target = np.zeros((batch_size, self.state_size))
 93         action, reward, done = [], [], []
 94 
 95         for i in range(batch_size):
 96             update_input[i] = min_batch[i][0]
 97             action.append(min_batch[i][1])
 98             reward.append(min_batch[i][2])
 99             done.append(min_batch[i][3])
100             update_target[i] = min_batch[i][4]
101 
102         target = self.model.predict(update_input, batch_size=batch_size)
103         target_val = self.model.predict(update_target, batch_size=batch_size)
104 
105         for i in range(self.batch_size):
106             if done[i]:
107                 target[i][action[i]] = reward[i]
108             else:
109                 target[i][action[i]] = reward[i] + self.gamma * np.amax(target_val[i])
110 
111         self.model.fit(update_input, target, batch_size=batch_size, epochs=1, verbose=0, callbacks=[self.history])
112         self.losses_list.append(self.history.losses[0])
113 
114 
115 def draw_score_plot(scores, filename='graph.png'):
116     fig = plt.figure()
117     ax1 = fig.add_subplot(1, 1, 1)
118     ax1.set_title('mean score')
119     ax1.plot(range(len(scores)), scores, color='blue')
120     plt.savefig(filename)
121 
122 
123 def draw_plot(scores, losses, filename='graph.png'):
124     fig = plt.figure()
125     ax1 = fig.add_subplot(1, 2, 1)
126     ax1.set_title('mean score')
127     ax1.plot(range(len(scores)), scores, color='blue')
128 
129 
130     ax2 = fig.add_subplot(1, 2, 2)
131     ax2.set_title('mean loss-reward')
132     ax2.plot(range(len(losses)), losses, color='blue')
133     plt.savefig(filename)
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posted @ 2019-02-25 21:30  Mr_chuan  阅读(977)  评论(0)    收藏  举报