update codes

codes/PolicyGradient/agent.py

@@ -17,9 +17,9 @@ from PolicyGradient.model import MLP
 
 class PolicyGradient:
     
-    def __init__(self, state_dim,cfg):
+    def __init__(self, n_states,cfg):
         self.gamma = cfg.gamma
-        self.policy_net = MLP(state_dim,hidden_dim=cfg.hidden_dim)
+        self.policy_net = MLP(n_states,hidden_dim=cfg.hidden_dim)
         self.optimizer = torch.optim.RMSprop(self.policy_net.parameters(), lr=cfg.lr)
         self.batch_size = cfg.batch_size
 

codes/PolicyGradient/model.py

@@ -19,7 +19,7 @@ class MLP(nn.Module):
     '''
     def __init__(self,input_dim,hidden_dim = 36):
         super(MLP, self).__init__()
-        # 24和36为hidden layer的层数，可根据input_dim, action_dim的情况来改变
+        # 24和36为hidden layer的层数，可根据input_dim, n_actions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

@@ -46,8 +46,8 @@ class PGConfig:
 def env_agent_config(cfg,seed=1):
     env = gym.make(cfg.env)  
     env.seed(seed)
-    state_dim = env.observation_space.shape[0]
-    agent = PolicyGradient(state_dim,cfg)
+    n_states = env.observation_space.shape[0]
+    agent = PolicyGradient(n_states,cfg)
     return env,agent
 
 def train(cfg,env,agent):