update

codes/A2C/test.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-20 17:43:17
 LastEditor: John
-LastEditTime: 2021-03-20 19:36:24
+LastEditTime: 2021-04-05 11:19:20
 Discription: 
 Environment: 
 '''
@@ -40,7 +40,7 @@ class A2CConfig:
         self.eval_eps = 200
         self.eval_steps = 200
         self.target_update = 4
-        self.hidden_dim=256
+        self.hidden_dim = 256
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     
     
@@ -76,7 +76,7 @@ class A2C:
 def train(cfg,env,agent):
     n_states = env.observation_space.shape[0]
     n_actions = env.action_space.n
-    actor_critic = ActorCritic(n_states, n_actions, hidden_dim)
+    actor_critic = ActorCritic(n_states, n_actions, cfg.hidden_dim)
     ac_optimizer = optim.Adam(actor_critic.parameters(), lr=learning_rate)
 
     all_lengths = []
@@ -112,7 +112,7 @@ def train(cfg,env,agent):
                 all_lengths.append(steps)
                 average_lengths.append(np.mean(all_lengths[-10:]))
                 if episode % 10 == 0:                    
-                    sys.stdout.write("episode: {}, reward: {}, total length: {}, average length: {} \n".format(episode, np.sum(rewards), steps, average_lengths[-1]))
+                    sys.stdout.write("episode: {}, reward: {}, total length: {}, average length: {} \n".format(episode, np.sum(rewards), steps+1, average_lengths[-1]))
                 break
         
         # compute Q values
@@ -154,7 +154,7 @@ def train(cfg,env,agent):
     plt.show()
 
 if __name__ == "__main__":
-    cfg = A2CConfig
+    cfg = A2CConfig()
     env = gym.make("CartPole-v0")
     n_states = env.observation_space.shape[0]
     n_actions = env.action_space.n