hot update PG

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: 2022-08-04 22:22:16
+LastEditTime: 2022-08-25 00:23:22
 Discription: 
 Environment: 
 '''
@@ -14,45 +14,51 @@ 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 sample(self, state):
+    def __init__(self,cfg):
+        self.n_actions = cfg['n_actions'] 
+        self.lr = cfg['lr']  
+        self.gamma = cfg['gamma']  
+        self.epsilon = cfg['epsilon_start']
+        self.sample_count = 0  
+        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(self.n_actions)) # Q table
+    def sample_action(self, state):
+        ''' another way to represent e-greedy policy
+        '''
         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])
+        best_action = np.argmax(self.Q_table[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(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  # 终止状态
-        else:
-            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
+    def predict_action(self,state):
+        ''' predict action while testing 
         '''
+        action = np.argmax(self.Q_table[state])
+        return action
+    def update(self, state, action, reward, next_state, next_action,done):
+        Q_predict = self.Q_table[state][action]
+        if done:
+            Q_target = reward  # terminal state
+        else:
+            Q_target = reward + self.gamma * self.Q_table[next_state][next_action] # the only difference from Q learning
+        self.Q_table[state][action] += self.lr * (Q_target - Q_predict) 
+    def save_model(self,path):
         import dill
+        from pathlib import Path
+        # create path
+        Path(path).mkdir(parents=True, exist_ok=True)
         torch.save(
-            obj=self.Q,
-            f=path+"sarsa_model.pkl",
+            obj=self.Q_table_table,
+            f=path+"checkpoint.pkl",
             pickle_module=dill
         )
-    def load(self, path):
-        '''从文件中读取数据到 Q表格
-        '''
+        print("Model saved!")
+    def load_model(self, path):
         import dill
-        self.Q =torch.load(f=path+'sarsa_model.pkl',pickle_module=dill)
+        self.Q_table_table =torch.load(f=path+'checkpoint.pkl',pickle_module=dill)
+        print("Mode loaded!")