update

codes/DoubleDQN/outputs/CartPole-v0/20220721-000842/results/params.json

@@ -1 +0,0 @@
-{"algo_name": "DoubleDQN", "env_name": "CartPole-v0", "train_eps": 200, "test_eps": 20, "gamma": 0.99, "epsilon_start": 0.95, "epsilon_end": 0.01, "epsilon_decay": 500, "lr": 0.0001, "memory_capacity": 100000, "batch_size": 64, "target_update": 2, "hidden_dim": 256, "device": "cuda", "result_path": "C:\\Users\\24438\\Desktop\\rl-tutorials\\codes\\DoubleDQN/outputs/CartPole-v0/20220721-000842/results/", "model_path": "C:\\Users\\24438\\Desktop\\rl-tutorials\\codes\\DoubleDQN/outputs/CartPole-v0/20220721-000842/models/", "save_fig": true}

codes/DoubleDQN/outputs/CartPole-v0/20220721-215416/results/params.json

@@ -0,0 +1,19 @@
+{
+    "algo_name": "DoubleDQN",
+    "env_name": "CartPole-v0",
+    "train_eps": 200,
+    "test_eps": 20,
+    "gamma": 0.99,
+    "epsilon_start": 0.95,
+    "epsilon_end": 0.01,
+    "epsilon_decay": 500,
+    "lr": 0.0001,
+    "memory_capacity": 100000,
+    "batch_size": 64,
+    "target_update": 2,
+    "hidden_dim": 256,
+    "device": "cuda",
+    "result_path": "C:\\Users\\24438\\Desktop\\rl-tutorials\\codes\\DoubleDQN/outputs/CartPole-v0/20220721-215416/results/",
+    "model_path": "C:\\Users\\24438\\Desktop\\rl-tutorials\\codes\\DoubleDQN/outputs/CartPole-v0/20220721-215416/models/",
+    "save_fig": true
+}

codes/DoubleDQN/task0.py

@@ -5,7 +5,7 @@ Author: JiangJi
 Email: johnjim0816@gmail.com
 Date: 2021-11-07 18:10:37
 LastEditor: JiangJi
-LastEditTime: 2022-07-21 00:08:38
+LastEditTime: 2022-07-21 21:52:31
 Discription: 
 '''
 import sys,os
@@ -86,7 +86,7 @@ def train(cfg,env,agent):
         else:
             ma_rewards.append(ep_reward)   
     print('Finish training!')
-    return rewards,ma_rewards
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
 
 def test(cfg,env,agent):
     print('Start testing')
@@ -115,22 +115,24 @@ def test(cfg,env,agent):
             ma_rewards.append(ep_reward)
         print(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
     print('Finish testing!')
-    return rewards,ma_rewards  
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
 
 if __name__ == "__main__":
     cfg = get_args()
     print(cfg.device)
     # training
     env,agent = env_agent_config(cfg,seed=1)
-    rewards, ma_rewards = train(cfg, env, agent)
+    res_dic = train(cfg, env, agent)
     make_dir(cfg.result_path, cfg.model_path)
     save_args(cfg)
     agent.save(path=cfg.model_path)
-    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
-    plot_rewards(rewards, ma_rewards, cfg, tag="train")  
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")  
     # testing
     env,agent = env_agent_config(cfg,seed=10)
     agent.load(path=cfg.model_path)
-    rewards,ma_rewards = test(cfg,env,agent)
-    save_results(rewards,ma_rewards,tag = 'test',path = cfg.result_path)
-    plot_rewards(rewards, ma_rewards, cfg, tag="test")  
+    res_dic = test(cfg,env,agent)
+    save_results(res_dic, tag='test',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test")