update projects

projects/codes/Sarsa/sarsa.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-12 16:58:16
+LastEditor: John
+LastEditTime: 2022-04-29 20:12:57
+Discription: 
+Environment: 
+'''
+import numpy as np
+from collections import defaultdict
+import torch
+import math
+class Sarsa(object):
+    def __init__(self,
+                 n_actions,cfg,):
+        self.n_actions = n_actions  
+        self.lr = cfg.lr  
+        self.gamma = cfg.gamma  
+        self.sample_count = 0 
+        self.epsilon_start = cfg.epsilon_start
+        self.epsilon_end = cfg.epsilon_end
+        self.epsilon_decay = cfg.epsilon_decay 
+        self.Q  = defaultdict(lambda: np.zeros(n_actions)) # Q table
+    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) # The probability to select a random action, is is log decayed
+        best_action = np.argmax(self.Q[state])
+        action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions
+        action_probs[best_action] += (1.0 - self.epsilon)
+        action = np.random.choice(np.arange(len(action_probs)), p=action_probs) 
+        return action
+    def predict_action(self,state):
+        return np.argmax(self.Q[state])
+    def update(self, state, action, reward, next_state, next_action,done):
+        Q_predict = self.Q[state][action]
+        if done:
+            Q_target = reward  # terminal state
+        else:
+            Q_target = reward + self.gamma * self.Q[next_state][next_action] 
+        self.Q[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,
+            f=path+"sarsa_model.pkl",
+            pickle_module=dill
+        )
+    def load(self, path):
+        '''从文件中读取数据到 Q表格
+        '''
+        import dill
+        self.Q =torch.load(f=path+'sarsa_model.pkl',pickle_module=dill)