update

codes/PolicyGradient/model.py

@@ -5,21 +5,22 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-23 16:35:58
 LastEditor: John
-LastEditTime: 2021-03-23 16:36:20
+LastEditTime: 2021-12-21 23:21:26
 Discription: 
 Environment: 
 '''
 import torch.nn as nn
 import torch.nn.functional as F
 class MLP(nn.Module):
+    
     ''' 多层感知机
         输入：state维度
         输出：概率
     '''
-    def __init__(self,state_dim,hidden_dim = 36):
+    def __init__(self,input_dim,hidden_dim = 36):
         super(MLP, self).__init__()
-        # 24和36为hidden layer的层数，可根据state_dim, action_dim的情况来改变
-        self.fc1 = nn.Linear(state_dim, hidden_dim)
+        # 24和36为hidden layer的层数，可根据input_dim, action_dim的情况来改变
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
         self.fc2 = nn.Linear(hidden_dim,hidden_dim)
         self.fc3 = nn.Linear(hidden_dim, 1)  # Prob of Left
 

codes/PolicyGradient/task0_train.py

@@ -34,7 +34,7 @@ class PGConfig:
         self.model_path = curr_path+"/outputs/" + self.env + \
             '/'+curr_time+'/models/'  # 保存模型的路径
         self.train_eps = 300 # 训练的回合数
-        self.eval_eps = 30 # 测试的回合数
+        self.test_eps = 30 # 测试的回合数
         self.batch_size = 8
         self.lr = 0.01 # 学习率
         self.gamma = 0.99
@@ -94,7 +94,7 @@ def eval(cfg,env,agent):
     print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
     rewards = []
     ma_rewards = []
-    for i_ep in range(cfg.eval_eps):
+    for i_ep in range(cfg.test_eps):
         state = env.reset()
         ep_reward = 0
         for _ in count():