62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Author: John
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Email: johnjim0816@gmail.com
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Date: 2020-09-11 23:03:00
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LastEditor: John
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LastEditTime: 2021-03-12 16:48:25
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Discription:
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Environment:
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'''
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import numpy as np
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import math
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import torch
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from collections import defaultdict
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class QLearning(object):
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def __init__(self,
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n_actions,cfg):
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self.n_actions = n_actions # number of actions
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self.lr = cfg.lr # learning rate
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self.gamma = cfg.gamma
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self.epsilon = 0
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self.sample_count = 0 # epsilon随训练的也就是采样次数逐渐衰减，所以需要计数
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self.epsilon_start = cfg.epsilon_start
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self.epsilon_end = cfg.epsilon_end
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self.epsilon_decay = cfg.epsilon_decay
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self.Q_table = defaultdict(lambda: np.zeros(n_actions)) # 使用字典存储Q表，个人比较喜欢这种，也可以用下面一行的二维数组表示，但是需要额外更改代码
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# self.Q_table = np.zeros((n_states, n_actions)) # Q表
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def choose_action(self, state):
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self.sample_count += 1
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self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
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math.exp(-1. * self.sample_count / self.epsilon_decay)
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# 随机选取0-1之间的值，如果大于epsilon就按照贪心策略选取action，否则随机选取
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if np.random.uniform(0, 1) > self.epsilon:
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action = np.argmax(self.Q_table[state])
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else:
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action = np.random.choice(self.n_actions) # 有一定概率随机探索选取一个动作
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return action
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def update(self, state, action, reward, next_state, done):
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Q_predict = self.Q_table[state][action]
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if done:
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Q_target = reward # terminal state
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else:
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Q_target = reward + self.gamma * np.max(
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self.Q_table[next_state]) # Q_table-learning
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self.Q_table[state][action] += self.lr * (Q_target - Q_predict)
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def save(self,path):
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'''把 Q表格 的数据保存到文件中
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'''
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import dill
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torch.save(
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obj=self.Q_table,
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f=path+"Qleaning_model.pkl",
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pickle_module=dill
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)
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def load(self, path):
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'''从文件中读取数据到 Q表格
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'''
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import dill
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self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill) |