update projects

projects/codes/Sarsa/sarsa.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-12 16:58:16
 LastEditor: John
-LastEditTime: 2022-04-29 20:12:57
+LastEditTime: 2022-08-04 22:22:16
 Discription: 
 Environment: 
 '''
@@ -15,7 +15,7 @@ import torch
 import math
 class Sarsa(object):
     def __init__(self,
-                 n_actions,cfg,):
+                 n_actions,cfg):
         self.n_actions = n_actions  
         self.lr = cfg.lr  
         self.gamma = cfg.gamma  
@@ -24,7 +24,7 @@ class Sarsa(object):
         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):
+    def sample(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
@@ -33,14 +33,14 @@ class Sarsa(object):
         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):
+    def predict(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
+            Q_target = reward  # 终止状态
         else:
-            Q_target = reward + self.gamma * self.Q[next_state][next_action] 
+            Q_target = reward + self.gamma * self.Q[next_state][next_action] # 与Q learning不同，Sarsa是拿下一步动作对应的Q值去更新
         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