add Qlearning

codes/QLearning/agent.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-09-11 23:03:00
+LastEditor: John
+LastEditTime: 2021-03-11 19:16:27
+Discription: 
+Environment: 
+'''
+from functools import update_wrapper
+import numpy as np
+import math
+import torch
+from collections import defaultdict
+
+class QLearning(object):
+    def __init__(self,
+                 n_actions,cfg):
+        self.n_actions = n_actions  # number of actions
+        self.lr = cfg.lr  # learning rate
+        self.gamma = cfg.gamma  
+        self.epsilon = 0 
+        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
+        self.epsilon_start = cfg.epsilon_start
+        self.epsilon_end = cfg.epsilon_end
+        self.epsilon_decay = cfg.epsilon_decay
+        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
+        # self.Q_table = np.zeros((n_states, n_actions))  # Q表
+    def choose_action(self, state):
+        self.sample_count += 1
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
+            math.exp(-1. * self.sample_count / self.epsilon_decay)
+        # 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
+        if np.random.uniform(0, 1) > self.epsilon:
+            action = np.argmax(self.Q_table[state])
+        else:
+            action = np.random.choice(self.n_actions)  # 有一定概率随机探索选取一个动作
+        return action
+            
+    def update(self, state, action, reward, next_state, done):
+        Q_predict = self.Q_table[state][action]
+        if done:
+            Q_target = reward  # terminal state
+        else:
+            Q_target = reward + self.gamma * np.max(
+                self.Q_table[next_state])  # Q_table-learning
+        self.Q_table[state][action] += self.lr * (Q_target - Q_predict)
+    def save(self,path):
+        '''把 Q表格 的数据保存到文件中
+        '''
+        import dill
+        torch.save(
+            obj=self.Q_table,
+            f=path,
+            pickle_module=dill
+        )
+
+    def load(self, path):
+        '''从文件中读取数据到 Q表格
+        '''
+        self.Q_table =torch.load(f='prod_dls.pkl',pickle_module=dill)