update

codes/A2C/env.py

@@ -14,16 +14,16 @@ import gym
 from A2C.multiprocessing_env import SubprocVecEnv
 
 # num_envs = 16
-# env_name = "Pendulum-v0"
+# env = "Pendulum-v0"
 
-def make_envs(num_envs=16,env_name="Pendulum-v0"):
+def make_envs(num_envs=16,env="Pendulum-v0"):
     ''' 创建多个子环境
     '''
     num_envs = 16
-    env_name = "CartPole-v0"
+    env = "CartPole-v0"
     def make_env():
         def _thunk():
-            env = gym.make(env_name)
+            env = gym.make(env)
             return env
 
         return _thunk
@@ -34,10 +34,10 @@ def make_envs(num_envs=16,env_name="Pendulum-v0"):
 # if __name__ == "__main__":
 
 #     num_envs = 16
-#     env_name = "CartPole-v0"
+#     env = "CartPole-v0"
 #     def make_env():
 #         def _thunk():
-#             env = gym.make(env_name)
+#             env = gym.make(env)
 #             return env
 
 #         return _thunk
@@ -45,4 +45,4 @@ def make_envs(num_envs=16,env_name="Pendulum-v0"):
 #     envs = [make_env() for i in range(num_envs)]
 #     envs = SubprocVecEnv(envs)
 if __name__ == "__main__":
-    envs = make_envs(num_envs=16,env_name="CartPole-v0") 
+    envs = make_envs(num_envs=16,env="CartPole-v0") 

codes/A2C/main.py

@@ -5,16 +5,20 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-11 20:58:21
 @LastEditor: John
-LastEditTime: 2021-03-20 16:58:04
+LastEditTime: 2021-04-05 11:14:39
 @Discription: 
 @Environment: python 3.7.9
 '''
 import sys,os
-sys.path.append(os.getcwd()) # add current terminal path
+curr_path = os.path.dirname(__file__)
+parent_path=os.path.dirname(curr_path) 
+sys.path.append(parent_path) # add current terminal path to sys.path
+
 import torch
 import gym
 import datetime
 from A2C.agent import A2C
+from common.utils import save_results,make_dir,del_empty_dir
 
 
 

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