hot update A2C

projects/codes/DQN/main.py

@@ -24,6 +24,7 @@ def get_args():
     parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
     parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
     parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
+    parser.add_argument('--ep_max_steps',default = 100000,type=int,help="steps per episode, much larger value can simulate infinite steps")
     parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor")
     parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
     parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
@@ -72,7 +73,7 @@ def train(cfg, env, agent):
         ep_reward = 0  # reward per episode
         ep_step = 0
         state = env.reset()  # reset and obtain initial state
-        while True:
+        for _ in range(cfg['ep_max_steps']):
             ep_step += 1
             action = agent.sample_action(state)  # sample action
             next_state, reward, done, _ = env.step(action)  # update env and return transitions
@@ -91,7 +92,7 @@ def train(cfg, env, agent):
             print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}')
     print("Finish training!")
     env.close()
-    res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
+    res_dic = {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
     return res_dic
 
 def test(cfg, env, agent):
@@ -103,7 +104,7 @@ def test(cfg, env, agent):
         ep_reward = 0  # reward per episode
         ep_step = 0
         state = env.reset()  # reset and obtain initial state
-        while True:
+        for _ in range(cfg['ep_max_steps']):
             ep_step+=1
             action = agent.predict_action(state)  # predict action
             next_state, reward, done, _ = env.step(action)  
@@ -116,7 +117,7 @@ def test(cfg, env, agent):
         print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}")
     print("Finish testing!")
     env.close()
-    return {'episodes':range(len(rewards)),'rewards':rewards}
+    return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
 
 
 if __name__ == "__main__":