johnjim0816 commited on May 16, 2023

Commit

ccb908b

•

1 Parent(s): 989b5fc

update CartPole-v1 DoubleDQN

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CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/config.yaml +0 -40
CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/logs/log.txt +0 -14
CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/results/learning_curve.png +0 -0
CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/results/res.csv +0 -11
CartPole-v1/Test_CartPole-v1_DoubleDQN_mp_20230406-160410/logs/log.txt +0 -52
CartPole-v1/Test_CartPole-v1_DoubleDQN_mp_20230406-160410/results/learning_curve.png +0 -0
CartPole-v1/Test_CartPole-v1_DoubleDQN_mp_20230406-160410/results/res.csv +0 -11
CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/config.yaml +0 -46
CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/logs/log.txt +0 -52
CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/results/learning_curve.png +0 -0
CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/results/res.csv +0 -11
CartPole-v1/{Test_CartPole-v1_DoubleDQN_mp_20230406-160410 → Test_single_CartPole-v1_DoubleDQN_20230516-115305}/config.yaml +20 -23
CartPole-v1/Test_single_CartPole-v1_DoubleDQN_20230516-115305/logs/log.txt +55 -0
CartPole-v1/{Test_CartPole-v1_DoubleDQN_mp_20230406-160410/models/checkpoint.pth → Test_single_CartPole-v1_DoubleDQN_20230516-115305/tb_logs/interact/events.out.tfevents.1684209185.JMac.local.52313.0} +2 -2
CartPole-v1/{Test_CartPole-v1_DoubleDQN_ray_20230406-170348/models/checkpoint.pt → Test_single_CartPole-v1_DoubleDQN_20230516-115305/tb_logs/model/events.out.tfevents.1684209185.JMac.local.52313.1} +2 -2
CartPole-v1/Train_CartPole-v1_DoubleDQN_20221122-125516/config.yaml +0 -40
CartPole-v1/Train_CartPole-v1_DoubleDQN_20221122-125516/logs/log.txt +0 -116
CartPole-v1/Train_CartPole-v1_DoubleDQN_20221122-125516/results/learning_curve.png +0 -0
CartPole-v1/Train_CartPole-v1_DoubleDQN_20221122-125516/results/res.csv +0 -101
CartPole-v1/Train_CartPole-v1_DoubleDQN_mp_20230406-160028/logs/log.txt +0 -42
CartPole-v1/Train_CartPole-v1_DoubleDQN_mp_20230406-160028/models/checkpoint.pth +0 -3
CartPole-v1/Train_CartPole-v1_DoubleDQN_mp_20230406-160028/results/learning_curve.png +0 -0
CartPole-v1/Train_CartPole-v1_DoubleDQN_mp_20230406-160028/results/res.csv +0 -402
CartPole-v1/Train_CartPole-v1_DoubleDQN_ray_20230406-162938/logs/log.txt +0 -42
CartPole-v1/Train_CartPole-v1_DoubleDQN_ray_20230406-162938/models/checkpoint.pt +0 -3
CartPole-v1/Train_CartPole-v1_DoubleDQN_ray_20230406-162938/results/learning_curve.png +0 -0
CartPole-v1/Train_CartPole-v1_DoubleDQN_ray_20230406-162938/results/res.csv +0 -401
CartPole-v1/{Train_CartPole-v1_DoubleDQN_ray_20230406-162938 → Train_ray_CartPole-v1_DoubleDQN_20230516-115126}/config.yaml +17 -20
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/logs/log.txt +157 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/1000 +0 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/1500 +0 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/2000 +0 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/2500 +0 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/3000 +0 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/3500 +0 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/500 +0 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/models/best +0 -0
CartPole-v1/{Test_CartPole-v1_DoubleDQN_20221122-125611/models/checkpoint.pth → Train_ray_CartPole-v1_DoubleDQN_20230516-115126/tb_logs/interact/events.out.tfevents.1684209086.JMac.local.52110.0} +2 -2
CartPole-v1/{Train_CartPole-v1_DoubleDQN_20221122-125516/models/checkpoint.pth → Train_ray_CartPole-v1_DoubleDQN_20230516-115126/tb_logs/interact/events.out.tfevents.1684209096.JMac.local.52161.0} +2 -2
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/tb_logs/model/events.out.tfevents.1684209086.JMac.local.52110.1 +3 -0
CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/tb_logs/model/events.out.tfevents.1684209096.JMac.local.52161.1 +3 -0
CartPole-v1/{Train_CartPole-v1_DoubleDQN_mp_20230406-160028 → Train_single_CartPole-v1_DoubleDQN_20230516-114540}/config.yaml +18 -21
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/logs/log.txt +162 -0
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/models/1000 +0 -0
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/models/1500 +0 -0
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/models/2000 +0 -0
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/models/2500 +0 -0
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/models/3000 +0 -0
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/models/3500 +0 -0
CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/models/4000 +0 -0

CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/config.yaml DELETED Viewed

@@ -1,40 +0,0 @@
-general_cfg:
-  algo_name: DoubleDQN
-  device: cuda
-  env_name: CartPole-v1
-  eval_eps: 10
-  eval_per_episode: 5
-  load_checkpoint: true
-  load_path: Train_CartPole-v1_DoubleDQN_20221122-125516
-  max_steps: 200
-  mode: test
-  save_fig: true
-  seed: 1
-  show_fig: false
-  test_eps: 10
-  train_eps: 100
-algo_cfg:
-  batch_size: 64
-  buffer_size: 100000
-  epsilon_decay: 500
-  epsilon_end: 0.01
-  epsilon_start: 0.95
-  gamma: 0.99
-  lr: 0.0001
-  target_update: 4
-  value_layers:
-  - activation: relu
-    layer_dim:
-    - n_states
-    - 256
-    layer_type: linear
-  - activation: relu
-    layer_dim:
-    - 256
-    - 256
-    layer_type: linear
-  - activation: none
-    layer_dim:
-    - 256
-    - n_actions
-    layer_type: linear

CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/logs/log.txt DELETED Viewed

@@ -1,14 +0,0 @@
-2022-11-22 12:56:12 - r - INFO: - n_states: 4, n_actions: 2
-2022-11-22 12:56:14 - r - INFO: - Start testing!
-2022-11-22 12:56:14 - r - INFO: - Env: CartPole-v1, Algorithm: DoubleDQN, Device: cuda
-2022-11-22 12:56:14 - r - INFO: - Episode: 1/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 2/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 3/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 4/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 5/10, Reward: 138.000, Step: 138
-2022-11-22 12:56:15 - r - INFO: - Episode: 6/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 7/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 8/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 9/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Episode: 10/10, Reward: 200.000, Step: 200
-2022-11-22 12:56:15 - r - INFO: - Finish testing!

CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/results/learning_curve.png DELETED Viewed

Binary file (31.4 kB)

CartPole-v1/Test_CartPole-v1_DoubleDQN_20221122-125611/results/res.csv DELETED Viewed

@@ -1,11 +0,0 @@
-episodes,rewards,steps
-0,200.0,200
-1,200.0,200
-2,200.0,200
-3,200.0,200
-4,138.0,138
-5,200.0,200
-6,200.0,200
-7,200.0,200
-8,200.0,200
-9,200.0,200

CartPole-v1/Test_CartPole-v1_DoubleDQN_mp_20230406-160410/logs/log.txt DELETED Viewed

@@ -1,52 +0,0 @@
-2023-04-06 16:04:10 - r - INFO: - Hyperparameters:
-2023-04-06 16:04:10 - r - INFO: - ================================================================================
-2023-04-06 16:04:10 - r - INFO: -         Name        	       Value        	        Type
-2023-04-06 16:04:10 - r - INFO: -       env_name      	    CartPole-v1     	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -     new_step_api    	         1          	   <class 'bool'>
-2023-04-06 16:04:10 - r - INFO: -       wrapper       	        None        	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -        render       	         0          	   <class 'bool'>
-2023-04-06 16:04:10 - r - INFO: -     render_mode     	       human        	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -      algo_name      	     DoubleDQN      	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -         mode        	        test        	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -      mp_backend     	         mp         	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -         seed        	         1          	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -        device       	        cpu         	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -      train_eps      	        400         	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -       test_eps      	         10         	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -       eval_eps      	         10         	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -   eval_per_episode  	         5          	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -      max_steps      	        200         	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -   load_checkpoint   	         1          	   <class 'bool'>
-2023-04-06 16:04:10 - r - INFO: -      load_path      	Train_CartPole-v1_DoubleDQN_mp_20230406-160028	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -       show_fig      	         0          	   <class 'bool'>
-2023-04-06 16:04:10 - r - INFO: -       save_fig      	         1          	   <class 'bool'>
-2023-04-06 16:04:10 - r - INFO: -      n_workers      	         1          	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -    epsilon_start    	        0.95        	  <class 'float'>
-2023-04-06 16:04:10 - r - INFO: -     epsilon_end     	        0.01        	  <class 'float'>
-2023-04-06 16:04:10 - r - INFO: -    epsilon_decay    	        500         	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -        gamma        	        0.95        	  <class 'float'>
-2023-04-06 16:04:10 - r - INFO: -          lr         	       0.0001       	  <class 'float'>
-2023-04-06 16:04:10 - r - INFO: -     buffer_size     	       100000       	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -      batch_size     	         64         	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -    target_update    	         4          	   <class 'int'>
-2023-04-06 16:04:10 - r - INFO: -     value_layers    	[{'layer_type': 'linear', 'layer_dim': ['n_states', 256], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [256, 256], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [256, 'n_actions'], 'activation': 'none'}]	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -       task_dir      	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-160410	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -       res_dir       	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-160410/results	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -       log_dir       	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-160410/logs	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -       traj_dir      	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-160410/traj	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: -      video_dir      	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-160410/videos	   <class 'str'>
-2023-04-06 16:04:10 - r - INFO: - ================================================================================
-2023-04-06 16:04:10 - r - INFO: - n_states: 4, n_actions: 2
-2023-04-06 16:04:10 - r - INFO: - Start testing!
-2023-04-06 16:04:10 - r - INFO: - Env: CartPole-v1, Algorithm: DoubleDQN, Device: cpu
-2023-04-06 16:04:10 - r - INFO: - Episode: 1/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 2/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 3/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 4/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 5/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 6/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 7/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 8/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 9/10, Reward: 200.000, Step: 200
-2023-04-06 16:04:10 - r - INFO: - Episode: 10/10, Reward: 198.000, Step: 198
-2023-04-06 16:04:10 - r - INFO: - Finish testing!

CartPole-v1/Test_CartPole-v1_DoubleDQN_mp_20230406-160410/results/learning_curve.png DELETED Viewed

Binary file (34.7 kB)

CartPole-v1/Test_CartPole-v1_DoubleDQN_mp_20230406-160410/results/res.csv DELETED Viewed

@@ -1,11 +0,0 @@
-episodes,rewards,steps
-0,200.0,200
-1,200.0,200
-2,200.0,200
-3,200.0,200
-4,200.0,200
-5,200.0,200
-6,200.0,200
-7,200.0,200
-8,200.0,200
-9,198.0,198

CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/config.yaml DELETED Viewed

@@ -1,46 +0,0 @@
-general_cfg:
-  algo_name: DoubleDQN
-  device: cpu
-  env_name: CartPole-v1
-  eval_eps: 10
-  eval_per_episode: 5
-  load_checkpoint: true
-  load_path: Train_CartPole-v1_DoubleDQN_ray_20230406-162938
-  max_steps: 200
-  mode: test
-  mp_backend: ray
-  n_workers: 1
-  new_step_api: true
-  render: false
-  render_mode: human
-  save_fig: true
-  seed: 1
-  show_fig: false
-  test_eps: 10
-  train_eps: 400
-  wrapper: null
-algo_cfg:
-  batch_size: 64
-  buffer_size: 100000
-  epsilon_decay: 500
-  epsilon_end: 0.01
-  epsilon_start: 0.95
-  gamma: 0.95
-  lr: 0.0001
-  target_update: 4
-  value_layers:
-  - activation: relu
-    layer_dim:
-    - n_states
-    - 256
-    layer_type: linear
-  - activation: relu
-    layer_dim:
-    - 256
-    - 256
-    layer_type: linear
-  - activation: none
-    layer_dim:
-    - 256
-    - n_actions
-    layer_type: linear

CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/logs/log.txt DELETED Viewed

@@ -1,52 +0,0 @@
-2023-04-06 17:03:48 - r - INFO: - Hyperparameters:
-2023-04-06 17:03:48 - r - INFO: - ================================================================================
-2023-04-06 17:03:48 - r - INFO: -         Name        	       Value        	        Type
-2023-04-06 17:03:48 - r - INFO: -       env_name      	    CartPole-v1     	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -     new_step_api    	         1          	   <class 'bool'>
-2023-04-06 17:03:48 - r - INFO: -       wrapper       	        None        	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -        render       	         0          	   <class 'bool'>
-2023-04-06 17:03:48 - r - INFO: -     render_mode     	       human        	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -      algo_name      	     DoubleDQN      	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -         mode        	        test        	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -      mp_backend     	        ray         	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -         seed        	         1          	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -        device       	        cpu         	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -      train_eps      	        400         	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -       test_eps      	         10         	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -       eval_eps      	         10         	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -   eval_per_episode  	         5          	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -      max_steps      	        200         	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -   load_checkpoint   	         1          	   <class 'bool'>
-2023-04-06 17:03:48 - r - INFO: -      load_path      	Train_CartPole-v1_DoubleDQN_ray_20230406-162938	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -       show_fig      	         0          	   <class 'bool'>
-2023-04-06 17:03:48 - r - INFO: -       save_fig      	         1          	   <class 'bool'>
-2023-04-06 17:03:48 - r - INFO: -      n_workers      	         1          	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -    epsilon_start    	        0.95        	  <class 'float'>
-2023-04-06 17:03:48 - r - INFO: -     epsilon_end     	        0.01        	  <class 'float'>
-2023-04-06 17:03:48 - r - INFO: -    epsilon_decay    	        500         	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -        gamma        	        0.95        	  <class 'float'>
-2023-04-06 17:03:48 - r - INFO: -          lr         	       0.0001       	  <class 'float'>
-2023-04-06 17:03:48 - r - INFO: -     buffer_size     	       100000       	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -      batch_size     	         64         	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -    target_update    	         4          	   <class 'int'>
-2023-04-06 17:03:48 - r - INFO: -     value_layers    	[{'layer_type': 'linear', 'layer_dim': ['n_states', 256], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [256, 256], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [256, 'n_actions'], 'activation': 'none'}]	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -       task_dir      	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-170348	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -       res_dir       	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-170348/results	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -       log_dir       	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-170348/logs	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -       traj_dir      	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-170348/traj	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: -      video_dir      	/media/disk/gsc/joyrl-offline/tasks/Test_CartPole-v1_DoubleDQN_20230406-170348/videos	   <class 'str'>
-2023-04-06 17:03:48 - r - INFO: - ================================================================================
-2023-04-06 17:03:48 - r - INFO: - n_states: 4, n_actions: 2
-2023-04-06 17:03:48 - r - INFO: - Start testing!
-2023-04-06 17:03:48 - r - INFO: - Env: CartPole-v1, Algorithm: DoubleDQN, Device: cpu
-2023-04-06 17:03:48 - r - INFO: - Episode: 1/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 2/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 3/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 4/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 5/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 6/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 7/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 8/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:48 - r - INFO: - Episode: 9/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:49 - r - INFO: - Episode: 10/10, Reward: 200.000, Step: 200
-2023-04-06 17:03:49 - r - INFO: - Finish testing!

CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/results/learning_curve.png DELETED Viewed

Binary file (27.7 kB)

CartPole-v1/Test_CartPole-v1_DoubleDQN_ray_20230406-170348/results/res.csv DELETED Viewed

@@ -1,11 +0,0 @@
-episodes,rewards,steps
-0,200.0,200
-1,200.0,200
-2,200.0,200
-3,200.0,200
-4,200.0,200
-5,200.0,200
-6,200.0,200
-7,200.0,200
-8,200.0,200
-9,200.0,200

CartPole-v1/{Test_CartPole-v1_DoubleDQN_mp_20230406-160410 → Test_single_CartPole-v1_DoubleDQN_20230516-115305}/config.yaml RENAMED Viewed

@@ -1,46 +1,43 @@
 general_cfg:
   algo_name: DoubleDQN
   device: cpu
-  env_name: CartPole-v1
-  eval_eps: 10
-  eval_per_episode: 5
   load_checkpoint: true
-  load_path: Train_CartPole-v1_DoubleDQN_mp_20230406-160028
-  max_steps: 200
   mode: test
-  mp_backend: mp
-  n_workers: 1
-  new_step_api: true
-  render: false
-  render_mode: human
-  save_fig: true
   seed: 1
-  show_fig: false
-  test_eps: 10
-  train_eps: 400
-  wrapper: null
 algo_cfg:
   batch_size: 64
   buffer_size: 100000
   epsilon_decay: 500
   epsilon_end: 0.01
   epsilon_start: 0.95
-  gamma: 0.95
   lr: 0.0001
   target_update: 4
   value_layers:
   - activation: relu
     layer_dim:
-    - n_states
     - 256
     layer_type: linear
   - activation: relu
     layer_dim:
     - 256
-    - 256
-    layer_type: linear
-  - activation: none
-    layer_dim:
-    - 256
-    - n_actions
     layer_type: linear

 general_cfg:
   algo_name: DoubleDQN
+  collect_traj: false
   device: cpu
+  env_name: gym
   load_checkpoint: true
+  load_model_step: best
+  load_path: Train_single_CartPole-v1_DoubleDQN_20230516-114540
+  max_episode: 10
+  max_step: 200
   mode: test
+  model_save_fre: 500
+  mp_backend: single
+  n_workers: 2
+  online_eval: true
+  online_eval_episode: 10
   seed: 1
 algo_cfg:
   batch_size: 64
   buffer_size: 100000
+  buffer_type: REPLAY_QUE
   epsilon_decay: 500
   epsilon_end: 0.01
   epsilon_start: 0.95
+  gamma: 0.99
   lr: 0.0001
   target_update: 4
   value_layers:
   - activation: relu
     layer_dim:
     - 256
     layer_type: linear
   - activation: relu
     layer_dim:
     - 256
     layer_type: linear
+env_cfg:
+  id: CartPole-v1
+  ignore_params:
+  - wrapper
+  - ignore_params
+  render_mode: null
+  wrapper: null

CartPole-v1/Test_single_CartPole-v1_DoubleDQN_20230516-115305/logs/log.txt ADDED Viewed

	@@ -0,0 +1,55 @@

+2023-05-16 11:53:05 - SimpleLog - INFO: - General Configs:
+2023-05-16 11:53:05 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:53:05 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:53:05 - SimpleLog - INFO: -       env_name      	        gym         	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -      algo_name      	     DoubleDQN      	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -         mode        	        test        	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -        device       	        cpu         	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -         seed        	         1          	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     max_episode     	         10         	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -       max_step      	        200         	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     collect_traj    	         0          	   <class 'bool'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -      mp_backend     	       single       	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -      n_workers      	         2          	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     online_eval     	         1          	   <class 'bool'>
+2023-05-16 11:53:05 - SimpleLog - INFO: - online_eval_episode 	         10         	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -    model_save_fre   	        500         	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -   load_checkpoint   	         1          	   <class 'bool'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -      load_path      	Train_single_CartPole-v1_DoubleDQN_20230516-114540	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -   load_model_step   	        best        	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:53:05 - SimpleLog - INFO: - Algo Configs:
+2023-05-16 11:53:05 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:53:05 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:53:05 - SimpleLog - INFO: -    epsilon_start    	        0.95        	  <class 'float'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     epsilon_end     	        0.01        	  <class 'float'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -    epsilon_decay    	        500         	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -        gamma        	        0.99        	  <class 'float'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -          lr         	       0.0001       	  <class 'float'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     buffer_size     	       100000       	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -      batch_size     	         64         	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -    target_update    	         4          	   <class 'int'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     value_layers    	[{'layer_type': 'linear', 'layer_dim': [256], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [256], 'activation': 'relu'}]	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     buffer_type     	     REPLAY_QUE     	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:53:05 - SimpleLog - INFO: - Env Configs:
+2023-05-16 11:53:05 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:53:05 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:53:05 - SimpleLog - INFO: -          id         	    CartPole-v1     	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -     render_mode     	        None        	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -       wrapper       	        None        	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: -    ignore_params    	['wrapper', 'ignore_params']	   <class 'str'>
+2023-05-16 11:53:05 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:53:05 - SimpleLog - INFO: - obs_space: Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32), n_actions: Discrete(2)
+2023-05-16 11:53:05 - SimpleLog - INFO: - Start testing!
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 0, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 1, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 2, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 3, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 4, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 5, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 6, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 7, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 8, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - episode: 9, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:53:05 - SimpleLog - INFO: - Finish testing! total time consumed: 0.24s

CartPole-v1/{Test_CartPole-v1_DoubleDQN_mp_20230406-160410/models/checkpoint.pth → Test_single_CartPole-v1_DoubleDQN_20230516-115305/tb_logs/interact/events.out.tfevents.1684209185.JMac.local.52313.0} RENAMED Viewed

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CartPole-v1/{Test_CartPole-v1_DoubleDQN_ray_20230406-170348/models/checkpoint.pt → Test_single_CartPole-v1_DoubleDQN_20230516-115305/tb_logs/model/events.out.tfevents.1684209185.JMac.local.52313.1} RENAMED Viewed

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CartPole-v1/Train_CartPole-v1_DoubleDQN_20221122-125516/config.yaml DELETED Viewed

@@ -1,40 +0,0 @@
-general_cfg:
-  algo_name: DoubleDQN
-  device: cuda
-  env_name: CartPole-v1
-  eval_eps: 10
-  eval_per_episode: 5
-  load_checkpoint: false
-  load_path: Train_CartPole-v1_DQN_20221026-054757
-  max_steps: 200
-  mode: train
-  save_fig: true
-  seed: 1
-  show_fig: false
-  test_eps: 10
-  train_eps: 100
-algo_cfg:
-  batch_size: 64
-  buffer_size: 100000
-  epsilon_decay: 500
-  epsilon_end: 0.01
-  epsilon_start: 0.95
-  gamma: 0.99
-  lr: 0.0001
-  target_update: 4
-  value_layers:
-  - activation: relu
-    layer_dim:
-    - n_states
-    - 256
-    layer_type: linear
-  - activation: relu
-    layer_dim:
-    - 256
-    - 256
-    layer_type: linear
-  - activation: none
-    layer_dim:
-    - 256
-    - n_actions
-    layer_type: linear

CartPole-v1/Train_CartPole-v1_DoubleDQN_20221122-125516/logs/log.txt DELETED Viewed

@@ -1,116 +0,0 @@
-2022-11-22 12:55:16 - r - INFO: - n_states: 4, n_actions: 2
-2022-11-22 12:55:19 - r - INFO: - Start training!
-2022-11-22 12:55:19 - r - INFO: - Env: CartPole-v1, Algorithm: DoubleDQN, Device: cuda
-2022-11-22 12:55:19 - r - INFO: - Episode: 1/100, Reward: 18.000, Step: 18
-2022-11-22 12:55:19 - r - INFO: - Episode: 2/100, Reward: 35.000, Step: 35
-2022-11-22 12:55:19 - r - INFO: - Episode: 3/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:19 - r - INFO: - Episode: 4/100, Reward: 32.000, Step: 32
-2022-11-22 12:55:19 - r - INFO: - Episode: 5/100, Reward: 16.000, Step: 16
-2022-11-22 12:55:19 - r - INFO: - Current episode 5 has the best eval reward: 9.100
-2022-11-22 12:55:19 - r - INFO: - Episode: 6/100, Reward: 9.000, Step: 9
-2022-11-22 12:55:19 - r - INFO: - Episode: 7/100, Reward: 12.000, Step: 12
-2022-11-22 12:55:19 - r - INFO: - Episode: 8/100, Reward: 16.000, Step: 16
-2022-11-22 12:55:19 - r - INFO: - Episode: 9/100, Reward: 14.000, Step: 14
-2022-11-22 12:55:19 - r - INFO: - Episode: 10/100, Reward: 12.000, Step: 12
-2022-11-22 12:55:19 - r - INFO: - Current episode 10 has the best eval reward: 9.200
-2022-11-22 12:55:19 - r - INFO: - Episode: 11/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:19 - r - INFO: - Episode: 12/100, Reward: 14.000, Step: 14
-2022-11-22 12:55:19 - r - INFO: - Episode: 13/100, Reward: 19.000, Step: 19
-2022-11-22 12:55:19 - r - INFO: - Episode: 14/100, Reward: 9.000, Step: 9
-2022-11-22 12:55:19 - r - INFO: - Episode: 15/100, Reward: 15.000, Step: 15
-2022-11-22 12:55:19 - r - INFO: - Current episode 15 has the best eval reward: 9.300
-2022-11-22 12:55:19 - r - INFO: - Episode: 16/100, Reward: 12.000, Step: 12
-2022-11-22 12:55:19 - r - INFO: - Episode: 17/100, Reward: 11.000, Step: 11
-2022-11-22 12:55:19 - r - INFO: - Episode: 18/100, Reward: 9.000, Step: 9
-2022-11-22 12:55:19 - r - INFO: - Episode: 19/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:19 - r - INFO: - Episode: 20/100, Reward: 17.000, Step: 17
-2022-11-22 12:55:19 - r - INFO: - Episode: 21/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:19 - r - INFO: - Episode: 22/100, Reward: 15.000, Step: 15
-2022-11-22 12:55:19 - r - INFO: - Episode: 23/100, Reward: 22.000, Step: 22
-2022-11-22 12:55:20 - r - INFO: - Episode: 24/100, Reward: 26.000, Step: 26
-2022-11-22 12:55:20 - r - INFO: - Episode: 25/100, Reward: 19.000, Step: 19
-2022-11-22 12:55:20 - r - INFO: - Current episode 25 has the best eval reward: 9.800
-2022-11-22 12:55:20 - r - INFO: - Episode: 26/100, Reward: 10.000, Step: 10
-2022-11-22 12:55:20 - r - INFO: - Episode: 27/100, Reward: 10.000, Step: 10
-2022-11-22 12:55:20 - r - INFO: - Episode: 28/100, Reward: 11.000, Step: 11
-2022-11-22 12:55:20 - r - INFO: - Episode: 29/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:20 - r - INFO: - Episode: 30/100, Reward: 16.000, Step: 16
-2022-11-22 12:55:20 - r - INFO: - Episode: 31/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:20 - r - INFO: - Episode: 32/100, Reward: 15.000, Step: 15
-2022-11-22 12:55:20 - r - INFO: - Episode: 33/100, Reward: 12.000, Step: 12
-2022-11-22 12:55:20 - r - INFO: - Episode: 34/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:20 - r - INFO: - Episode: 35/100, Reward: 13.000, Step: 13
-2022-11-22 12:55:20 - r - INFO: - Episode: 36/100, Reward: 11.000, Step: 11
-2022-11-22 12:55:20 - r - INFO: - Episode: 37/100, Reward: 9.000, Step: 9
-2022-11-22 12:55:20 - r - INFO: - Episode: 38/100, Reward: 9.000, Step: 9
-2022-11-22 12:55:20 - r - INFO: - Episode: 39/100, Reward: 10.000, Step: 10
-2022-11-22 12:55:20 - r - INFO: - Episode: 40/100, Reward: 14.000, Step: 14
-2022-11-22 12:55:20 - r - INFO: - Episode: 41/100, Reward: 9.000, Step: 9
-2022-11-22 12:55:20 - r - INFO: - Episode: 42/100, Reward: 10.000, Step: 10
-2022-11-22 12:55:20 - r - INFO: - Episode: 43/100, Reward: 9.000, Step: 9
-2022-11-22 12:55:20 - r - INFO: - Episode: 44/100, Reward: 14.000, Step: 14
-2022-11-22 12:55:20 - r - INFO: - Episode: 45/100, Reward: 10.000, Step: 10
-2022-11-22 12:55:20 - r - INFO: - Episode: 46/100, Reward: 19.000, Step: 19
-2022-11-22 12:55:20 - r - INFO: - Episode: 47/100, Reward: 10.000, Step: 10
-2022-11-22 12:55:20 - r - INFO: - Episode: 48/100, Reward: 14.000, Step: 14
-2022-11-22 12:55:20 - r - INFO: - Episode: 49/100, Reward: 18.000, Step: 18
-2022-11-22 12:55:20 - r - INFO: - Episode: 50/100, Reward: 32.000, Step: 32
-2022-11-22 12:55:20 - r - INFO: - Current episode 50 has the best eval reward: 24.300
-2022-11-22 12:55:21 - r - INFO: - Episode: 51/100, Reward: 17.000, Step: 17
-2022-11-22 12:55:21 - r - INFO: - Episode: 52/100, Reward: 15.000, Step: 15
-2022-11-22 12:55:21 - r - INFO: - Episode: 53/100, Reward: 18.000, Step: 18
-2022-11-22 12:55:21 - r - INFO: - Episode: 54/100, Reward: 14.000, Step: 14
-2022-11-22 12:55:21 - r - INFO: - Episode: 55/100, Reward: 22.000, Step: 22
-2022-11-22 12:55:21 - r - INFO: - Episode: 56/100, Reward: 14.000, Step: 14
-2022-11-22 12:55:21 - r - INFO: - Episode: 57/100, Reward: 21.000, Step: 21
-2022-11-22 12:55:21 - r - INFO: - Episode: 58/100, Reward: 21.000, Step: 21
-2022-11-22 12:55:21 - r - INFO: - Episode: 59/100, Reward: 23.000, Step: 23
-2022-11-22 12:55:21 - r - INFO: - Episode: 60/100, Reward: 21.000, Step: 21
-2022-11-22 12:55:21 - r - INFO: - Episode: 61/100, Reward: 21.000, Step: 21
-2022-11-22 12:55:21 - r - INFO: - Episode: 62/100, Reward: 35.000, Step: 35
-2022-11-22 12:55:21 - r - INFO: - Episode: 63/100, Reward: 23.000, Step: 23
-2022-11-22 12:55:21 - r - INFO: - Episode: 64/100, Reward: 27.000, Step: 27
-2022-11-22 12:55:21 - r - INFO: - Episode: 65/100, Reward: 24.000, Step: 24
-2022-11-22 12:55:21 - r - INFO: - Current episode 65 has the best eval reward: 29.700
-2022-11-22 12:55:21 - r - INFO: - Episode: 66/100, Reward: 28.000, Step: 28
-2022-11-22 12:55:21 - r - INFO: - Episode: 67/100, Reward: 30.000, Step: 30
-2022-11-22 12:55:22 - r - INFO: - Episode: 68/100, Reward: 33.000, Step: 33
-2022-11-22 12:55:22 - r - INFO: - Episode: 69/100, Reward: 33.000, Step: 33
-2022-11-22 12:55:22 - r - INFO: - Episode: 70/100, Reward: 26.000, Step: 26
-2022-11-22 12:55:22 - r - INFO: - Current episode 70 has the best eval reward: 34.400
-2022-11-22 12:55:22 - r - INFO: - Episode: 71/100, Reward: 37.000, Step: 37
-2022-11-22 12:55:22 - r - INFO: - Episode: 72/100, Reward: 28.000, Step: 28
-2022-11-22 12:55:22 - r - INFO: - Episode: 73/100, Reward: 30.000, Step: 30
-2022-11-22 12:55:22 - r - INFO: - Episode: 74/100, Reward: 41.000, Step: 41
-2022-11-22 12:55:22 - r - INFO: - Episode: 75/100, Reward: 45.000, Step: 45
-2022-11-22 12:55:22 - r - INFO: - Current episode 75 has the best eval reward: 35.600
-2022-11-22 12:55:23 - r - INFO: - Episode: 76/100, Reward: 68.000, Step: 68
-2022-11-22 12:55:23 - r - INFO: - Episode: 77/100, Reward: 33.000, Step: 33
-2022-11-22 12:55:23 - r - INFO: - Episode: 78/100, Reward: 46.000, Step: 46
-2022-11-22 12:55:23 - r - INFO: - Episode: 79/100, Reward: 54.000, Step: 54
-2022-11-22 12:55:23 - r - INFO: - Episode: 80/100, Reward: 37.000, Step: 37
-2022-11-22 12:55:23 - r - INFO: - Current episode 80 has the best eval reward: 42.800
-2022-11-22 12:55:23 - r - INFO: - Episode: 81/100, Reward: 43.000, Step: 43
-2022-11-22 12:55:23 - r - INFO: - Episode: 82/100, Reward: 79.000, Step: 79
-2022-11-22 12:55:23 - r - INFO: - Episode: 83/100, Reward: 36.000, Step: 36
-2022-11-22 12:55:24 - r - INFO: - Episode: 84/100, Reward: 58.000, Step: 58
-2022-11-22 12:55:24 - r - INFO: - Episode: 85/100, Reward: 42.000, Step: 42
-2022-11-22 12:55:24 - r - INFO: - Current episode 85 has the best eval reward: 62.100
-2022-11-22 12:55:24 - r - INFO: - Episode: 86/100, Reward: 136.000, Step: 136
-2022-11-22 12:55:24 - r - INFO: - Episode: 87/100, Reward: 57.000, Step: 57
-2022-11-22 12:55:24 - r - INFO: - Episode: 88/100, Reward: 46.000, Step: 46
-2022-11-22 12:55:25 - r - INFO: - Episode: 89/100, Reward: 105.000, Step: 105
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-2023-04-06 16:29:38 - r - INFO: -       task_dir      	/media/disk/gsc/joyrl-offline/tasks/Train_CartPole-v1_DoubleDQN_20230406-162938	   <class 'str'>
-2023-04-06 16:29:38 - r - INFO: -       res_dir       	/media/disk/gsc/joyrl-offline/tasks/Train_CartPole-v1_DoubleDQN_20230406-162938/results	   <class 'str'>
-2023-04-06 16:29:38 - r - INFO: -       log_dir       	/media/disk/gsc/joyrl-offline/tasks/Train_CartPole-v1_DoubleDQN_20230406-162938/logs	   <class 'str'>
-2023-04-06 16:29:38 - r - INFO: -       traj_dir      	/media/disk/gsc/joyrl-offline/tasks/Train_CartPole-v1_DoubleDQN_20230406-162938/traj	   <class 'str'>
-2023-04-06 16:29:38 - r - INFO: -      video_dir      	/media/disk/gsc/joyrl-offline/tasks/Train_CartPole-v1_DoubleDQN_20230406-162938/videos	   <class 'str'>
-2023-04-06 16:29:38 - r - INFO: - ================================================================================
-2023-04-06 16:29:40 - r - INFO: - n_states: 4, n_actions: 2
-2023-04-06 16:29:40 - r - INFO: - Start training!
-2023-04-06 16:29:40 - r - INFO: - Env: CartPole-v1, Algorithm: DoubleDQN, Device: cpu
-2023-04-06 16:37:19 - r - INFO: - Finish training!

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CartPole-v1/{Train_CartPole-v1_DoubleDQN_ray_20230406-162938 → Train_ray_CartPole-v1_DoubleDQN_20230516-115126}/config.yaml RENAMED Viewed

@@ -1,46 +1,43 @@
 general_cfg:
   algo_name: DoubleDQN
   device: cpu
-  env_name: CartPole-v1
-  eval_eps: 10
-  eval_per_episode: 5
   load_checkpoint: false
   load_path: Train_CartPole-v1_DQN_20221026-054757
-  max_steps: 200
   mode: train
   mp_backend: ray
   n_workers: 2
-  new_step_api: true
-  render: false
-  render_mode: human
-  save_fig: true
   seed: 1
-  show_fig: false
-  test_eps: 10
-  train_eps: 400
-  wrapper: null
 algo_cfg:
   batch_size: 64
   buffer_size: 100000
   epsilon_decay: 500
   epsilon_end: 0.01
   epsilon_start: 0.95
-  gamma: 0.95
   lr: 0.0001
   target_update: 4
   value_layers:
   - activation: relu
     layer_dim:
-    - n_states
     - 256
     layer_type: linear
   - activation: relu
     layer_dim:
     - 256
-    - 256
-    layer_type: linear
-  - activation: none
-    layer_dim:
-    - 256
-    - n_actions
     layer_type: linear

 general_cfg:
   algo_name: DoubleDQN
+  collect_traj: false
   device: cpu
+  env_name: gym
   load_checkpoint: false
+  load_model_step: best
   load_path: Train_CartPole-v1_DQN_20221026-054757
+  max_episode: 100
+  max_step: 200
   mode: train
+  model_save_fre: 500
   mp_backend: ray
   n_workers: 2
+  online_eval: true
+  online_eval_episode: 10
   seed: 1
 algo_cfg:
   batch_size: 64
   buffer_size: 100000
+  buffer_type: REPLAY_QUE
   epsilon_decay: 500
   epsilon_end: 0.01
   epsilon_start: 0.95
+  gamma: 0.99
   lr: 0.0001
   target_update: 4
   value_layers:
   - activation: relu
     layer_dim:
     - 256
     layer_type: linear
   - activation: relu
     layer_dim:
     - 256
     layer_type: linear
+env_cfg:
+  id: CartPole-v1
+  ignore_params:
+  - wrapper
+  - ignore_params
+  render_mode: null
+  wrapper: null

CartPole-v1/Train_ray_CartPole-v1_DoubleDQN_20230516-115126/logs/log.txt ADDED Viewed

	@@ -0,0 +1,157 @@

+2023-05-16 11:51:26 - SimpleLog - INFO: - General Configs:
+2023-05-16 11:51:26 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:51:26 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:51:26 - SimpleLog - INFO: -       env_name      	        gym         	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -      algo_name      	     DoubleDQN      	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -         mode        	       train        	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -        device       	        cpu         	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -         seed        	         1          	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     max_episode     	        100         	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -       max_step      	        200         	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     collect_traj    	         0          	   <class 'bool'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -      mp_backend     	        ray         	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -      n_workers      	         2          	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     online_eval     	         1          	   <class 'bool'>
+2023-05-16 11:51:26 - SimpleLog - INFO: - online_eval_episode 	         10         	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -    model_save_fre   	        500         	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -   load_checkpoint   	         0          	   <class 'bool'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -      load_path      	Train_CartPole-v1_DQN_20221026-054757	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -   load_model_step   	        best        	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:51:26 - SimpleLog - INFO: - Algo Configs:
+2023-05-16 11:51:26 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:51:26 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:51:26 - SimpleLog - INFO: -    epsilon_start    	        0.95        	  <class 'float'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     epsilon_end     	        0.01        	  <class 'float'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -    epsilon_decay    	        500         	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -        gamma        	        0.99        	  <class 'float'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -          lr         	       0.0001       	  <class 'float'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     buffer_size     	       100000       	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -      batch_size     	         64         	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -    target_update    	         4          	   <class 'int'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     value_layers    	[{'layer_type': 'linear', 'layer_dim': [256], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [256], 'activation': 'relu'}]	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     buffer_type     	     REPLAY_QUE     	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:51:26 - SimpleLog - INFO: - Env Configs:
+2023-05-16 11:51:26 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:51:26 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:51:26 - SimpleLog - INFO: -          id         	    CartPole-v1     	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -     render_mode     	        None        	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -       wrapper       	        None        	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: -    ignore_params    	['wrapper', 'ignore_params']	   <class 'str'>
+2023-05-16 11:51:26 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:51:32 - SimpleLog - INFO: - obs_space: Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32), n_actions: Discrete(2)
+2023-05-16 11:51:37 - RayLog - INFO: - Worker 0 finished episode 0 with reward 16.0 in 16 steps
+2023-05-16 11:51:37 - RayLog - INFO: - Worker 1 finished episode 0 with reward 20.0 in 20 steps
+2023-05-16 11:51:39 - RayLog - INFO: - Worker 0 finished episode 2 with reward 11.0 in 11 steps
+2023-05-16 11:51:39 - RayLog - INFO: - Worker 1 finished episode 2 with reward 15.0 in 15 steps
+2023-05-16 11:51:39 - RayLog - INFO: - Worker 1 finished episode 4 with reward 13.0 in 13 steps
+2023-05-16 11:51:39 - RayLog - INFO: - Worker 0 finished episode 3 with reward 22.0 in 22 steps
+2023-05-16 11:51:39 - RayLog - INFO: - Worker 1 finished episode 5 with reward 9.0 in 9 steps
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+2023-05-16 11:51:40 - RayLog - INFO: - Worker 1 finished episode 9 with reward 13.0 in 13 steps
+2023-05-16 11:51:40 - RayLog - INFO: - Worker 1 finished episode 10 with reward 14.0 in 14 steps
+2023-05-16 11:51:40 - RayLog - INFO: - Worker 0 finished episode 8 with reward 35.0 in 35 steps
+2023-05-16 11:51:40 - RayLog - INFO: - Worker 1 finished episode 11 with reward 13.0 in 13 steps
+2023-05-16 11:51:40 - RayLog - INFO: - Worker 0 finished episode 12 with reward 15.0 in 15 steps
+2023-05-16 11:51:40 - RayLog - INFO: - Worker 1 finished episode 13 with reward 12.0 in 12 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 0 finished episode 14 with reward 10.0 in 10 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 1 finished episode 15 with reward 11.0 in 11 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 0 finished episode 16 with reward 11.0 in 11 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 0 finished episode 18 with reward 12.0 in 12 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 1 finished episode 17 with reward 21.0 in 21 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 0 finished episode 19 with reward 15.0 in 15 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 1 finished episode 20 with reward 18.0 in 18 steps
+2023-05-16 11:51:41 - RayLog - INFO: - Worker 0 finished episode 21 with reward 13.0 in 13 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 1 finished episode 22 with reward 10.0 in 10 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 0 finished episode 23 with reward 16.0 in 16 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 1 finished episode 24 with reward 19.0 in 19 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 0 finished episode 25 with reward 10.0 in 10 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 0 finished episode 27 with reward 10.0 in 10 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 0 finished episode 28 with reward 10.0 in 10 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 1 finished episode 26 with reward 26.0 in 26 steps
+2023-05-16 11:51:42 - RayLog - INFO: - Worker 0 finished episode 29 with reward 12.0 in 12 steps
+2023-05-16 11:51:43 - RayLog - INFO: - Worker 1 finished episode 30 with reward 15.0 in 15 steps
+2023-05-16 11:51:43 - RayLog - INFO: - Worker 1 finished episode 32 with reward 10.0 in 10 steps
+2023-05-16 11:51:43 - RayLog - INFO: - Worker 0 finished episode 31 with reward 18.0 in 18 steps
+2023-05-16 11:51:43 - RayLog - INFO: - Worker 1 finished episode 33 with reward 13.0 in 13 steps
+2023-05-16 11:51:43 - RayLog - INFO: - Worker 0 finished episode 34 with reward 10.0 in 10 steps
+2023-05-16 11:51:43 - RayLog - INFO: - Worker 0 finished episode 36 with reward 9.0 in 9 steps
+2023-05-16 11:51:43 - RayLog - INFO: - Worker 1 finished episode 35 with reward 12.0 in 12 steps
+2023-05-16 11:51:45 - RayLog - INFO: - update_step: 500, online_eval_reward: 9.000
+2023-05-16 11:51:45 - RayLog - INFO: - current update step obtain a better online_eval_reward: 9.000, save the best model!
+2023-05-16 11:51:45 - RayLog - INFO: - Worker 1 finished episode 38 with reward 10.0 in 10 steps
+2023-05-16 11:51:45 - RayLog - INFO: - Worker 0 finished episode 37 with reward 13.0 in 13 steps
+2023-05-16 11:51:45 - RayLog - INFO: - Worker 0 finished episode 40 with reward 9.0 in 9 steps
+2023-05-16 11:51:45 - RayLog - INFO: - Worker 1 finished episode 39 with reward 14.0 in 14 steps
+2023-05-16 11:51:45 - RayLog - INFO: - Worker 0 finished episode 41 with reward 14.0 in 14 steps
+2023-05-16 11:51:45 - RayLog - INFO: - Worker 1 finished episode 42 with reward 20.0 in 20 steps
+2023-05-16 11:51:45 - RayLog - INFO: - Worker 0 finished episode 43 with reward 15.0 in 15 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 1 finished episode 44 with reward 19.0 in 19 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 0 finished episode 45 with reward 17.0 in 17 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 0 finished episode 47 with reward 12.0 in 12 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 1 finished episode 46 with reward 15.0 in 15 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 0 finished episode 48 with reward 14.0 in 14 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 1 finished episode 49 with reward 16.0 in 16 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 0 finished episode 50 with reward 9.0 in 9 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 1 finished episode 51 with reward 13.0 in 13 steps
+2023-05-16 11:51:46 - RayLog - INFO: - Worker 0 finished episode 52 with reward 10.0 in 10 steps
+2023-05-16 11:51:47 - RayLog - INFO: - Worker 1 finished episode 53 with reward 13.0 in 13 steps
+2023-05-16 11:51:47 - RayLog - INFO: - Worker 0 finished episode 54 with reward 13.0 in 13 steps
+2023-05-16 11:51:47 - RayLog - INFO: - Worker 0 finished episode 56 with reward 14.0 in 14 steps
+2023-05-16 11:51:47 - RayLog - INFO: - Worker 1 finished episode 55 with reward 20.0 in 20 steps
+2023-05-16 11:51:47 - RayLog - INFO: - Worker 0 finished episode 57 with reward 14.0 in 14 steps
+2023-05-16 11:51:47 - RayLog - INFO: - Worker 1 finished episode 58 with reward 16.0 in 16 steps
+2023-05-16 11:51:47 - RayLog - INFO: - Worker 0 finished episode 59 with reward 11.0 in 11 steps
+2023-05-16 11:51:48 - RayLog - INFO: - Worker 1 finished episode 60 with reward 14.0 in 14 steps
+2023-05-16 11:51:48 - RayLog - INFO: - Worker 0 finished episode 61 with reward 9.0 in 9 steps
+2023-05-16 11:51:48 - RayLog - INFO: - Worker 0 finished episode 63 with reward 19.0 in 19 steps
+2023-05-16 11:51:48 - RayLog - INFO: - Worker 1 finished episode 62 with reward 23.0 in 23 steps
+2023-05-16 11:51:48 - RayLog - INFO: - Worker 0 finished episode 64 with reward 18.0 in 18 steps
+2023-05-16 11:51:49 - RayLog - INFO: - Worker 1 finished episode 65 with reward 26.0 in 26 steps
+2023-05-16 11:51:49 - RayLog - INFO: - update_step: 1000, online_eval_reward: 69.000
+2023-05-16 11:51:49 - RayLog - INFO: - current update step obtain a better online_eval_reward: 69.000, save the best model!
+2023-05-16 11:51:50 - RayLog - INFO: - Worker 1 finished episode 67 with reward 93.0 in 93 steps
+2023-05-16 11:51:51 - RayLog - INFO: - Worker 0 finished episode 66 with reward 127.0 in 127 steps
+2023-05-16 11:51:51 - RayLog - INFO: - Worker 1 finished episode 68 with reward 40.0 in 40 steps
+2023-05-16 11:51:52 - RayLog - INFO: - Worker 0 finished episode 69 with reward 54.0 in 54 steps
+2023-05-16 11:51:52 - RayLog - INFO: - Worker 1 finished episode 70 with reward 48.0 in 48 steps
+2023-05-16 11:51:53 - RayLog - INFO: - Worker 0 finished episode 71 with reward 62.0 in 62 steps
+2023-05-16 11:51:53 - RayLog - INFO: - Worker 1 finished episode 72 with reward 60.0 in 60 steps
+2023-05-16 11:51:54 - RayLog - INFO: - Worker 1 finished episode 74 with reward 35.0 in 35 steps
+2023-05-16 11:51:54 - RayLog - INFO: - Worker 0 finished episode 73 with reward 47.0 in 47 steps
+2023-05-16 11:51:54 - RayLog - INFO: - update_step: 1500, online_eval_reward: 63.000
+2023-05-16 11:51:54 - RayLog - INFO: - Worker 1 finished episode 75 with reward 38.0 in 38 steps
+2023-05-16 11:51:54 - RayLog - INFO: - Worker 0 finished episode 76 with reward 46.0 in 46 steps
+2023-05-16 11:51:55 - RayLog - INFO: - Worker 1 finished episode 77 with reward 40.0 in 40 steps
+2023-05-16 11:51:55 - RayLog - INFO: - Worker 0 finished episode 78 with reward 57.0 in 57 steps
+2023-05-16 11:51:56 - RayLog - INFO: - Worker 1 finished episode 79 with reward 38.0 in 38 steps
+2023-05-16 11:51:56 - RayLog - INFO: - Worker 1 finished episode 81 with reward 33.0 in 33 steps
+2023-05-16 11:51:56 - RayLog - INFO: - Worker 0 finished episode 80 with reward 51.0 in 51 steps
+2023-05-16 11:51:57 - RayLog - INFO: - Worker 1 finished episode 82 with reward 44.0 in 44 steps
+2023-05-16 11:51:58 - RayLog - INFO: - Worker 0 finished episode 83 with reward 70.0 in 70 steps
+2023-05-16 11:51:58 - RayLog - INFO: - Worker 1 finished episode 84 with reward 55.0 in 55 steps
+2023-05-16 11:51:58 - RayLog - INFO: - update_step: 2000, online_eval_reward: 82.000
+2023-05-16 11:51:58 - RayLog - INFO: - current update step obtain a better online_eval_reward: 82.000, save the best model!
+2023-05-16 11:51:59 - RayLog - INFO: - Worker 0 finished episode 85 with reward 66.0 in 66 steps
+2023-05-16 11:51:59 - RayLog - INFO: - Worker 1 finished episode 86 with reward 56.0 in 56 steps
+2023-05-16 11:52:00 - RayLog - INFO: - Worker 1 finished episode 88 with reward 45.0 in 45 steps
+2023-05-16 11:52:00 - RayLog - INFO: - Worker 0 finished episode 87 with reward 68.0 in 68 steps
+2023-05-16 11:52:01 - RayLog - INFO: - Worker 1 finished episode 89 with reward 50.0 in 50 steps
+2023-05-16 11:52:02 - RayLog - INFO: - Worker 0 finished episode 90 with reward 79.0 in 79 steps
+2023-05-16 11:52:02 - RayLog - INFO: - Worker 1 finished episode 91 with reward 57.0 in 57 steps
+2023-05-16 11:52:04 - RayLog - INFO: - update_step: 2500, online_eval_reward: 77.000
+2023-05-16 11:52:04 - RayLog - INFO: - Worker 1 finished episode 93 with reward 66.0 in 66 steps
+2023-05-16 11:52:04 - RayLog - INFO: - Worker 0 finished episode 92 with reward 84.0 in 84 steps
+2023-05-16 11:52:05 - RayLog - INFO: - Worker 1 finished episode 94 with reward 56.0 in 56 steps
+2023-05-16 11:52:07 - RayLog - INFO: - Worker 0 finished episode 95 with reward 134.0 in 134 steps
+2023-05-16 11:52:08 - RayLog - INFO: - Worker 1 finished episode 96 with reward 115.0 in 115 steps
+2023-05-16 11:52:10 - RayLog - INFO: - update_step: 3000, online_eval_reward: 200.000
+2023-05-16 11:52:10 - RayLog - INFO: - current update step obtain a better online_eval_reward: 200.000, save the best model!
+2023-05-16 11:52:12 - RayLog - INFO: - Worker 0 finished episode 97 with reward 200.0 in 200 steps
+2023-05-16 11:52:13 - RayLog - INFO: - Worker 1 finished episode 98 with reward 200.0 in 200 steps
+2023-05-16 11:52:15 - RayLog - INFO: - update_step: 3500, online_eval_reward: 200.000
+2023-05-16 11:52:16 - RayLog - INFO: - Worker 0 finished episode 99 with reward 200.0 in 200 steps
+2023-05-16 11:52:17 - RayLog - INFO: - Worker 1 finished episode 100 with reward 200.0 in 200 steps
+2023-05-16 11:52:19 - SimpleLog - INFO: - Finish training! total time consumed: 53.70s

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CartPole-v1/{Train_CartPole-v1_DoubleDQN_mp_20230406-160028 → Train_single_CartPole-v1_DoubleDQN_20230516-114540}/config.yaml RENAMED Viewed

@@ -1,46 +1,43 @@
 general_cfg:
   algo_name: DoubleDQN
   device: cpu
-  env_name: CartPole-v1
-  eval_eps: 10
-  eval_per_episode: 5
   load_checkpoint: false
   load_path: Train_CartPole-v1_DQN_20221026-054757
-  max_steps: 200
   mode: train
-  mp_backend: mp
   n_workers: 2
-  new_step_api: true
-  render: false
-  render_mode: human
-  save_fig: true
   seed: 1
-  show_fig: false
-  test_eps: 10
-  train_eps: 400
-  wrapper: null
 algo_cfg:
   batch_size: 64
   buffer_size: 100000
   epsilon_decay: 500
   epsilon_end: 0.01
   epsilon_start: 0.95
-  gamma: 0.95
   lr: 0.0001
   target_update: 4
   value_layers:
   - activation: relu
     layer_dim:
-    - n_states
     - 256
     layer_type: linear
   - activation: relu
     layer_dim:
     - 256
-    - 256
-    layer_type: linear
-  - activation: none
-    layer_dim:
-    - 256
-    - n_actions
     layer_type: linear

 general_cfg:
   algo_name: DoubleDQN
+  collect_traj: false
   device: cpu
+  env_name: gym
   load_checkpoint: false
+  load_model_step: best
   load_path: Train_CartPole-v1_DQN_20221026-054757
+  max_episode: 100
+  max_step: 200
   mode: train
+  model_save_fre: 500
+  mp_backend: single
   n_workers: 2
+  online_eval: true
+  online_eval_episode: 10
   seed: 1
 algo_cfg:
   batch_size: 64
   buffer_size: 100000
+  buffer_type: REPLAY_QUE
   epsilon_decay: 500
   epsilon_end: 0.01
   epsilon_start: 0.95
+  gamma: 0.99
   lr: 0.0001
   target_update: 4
   value_layers:
   - activation: relu
     layer_dim:
     - 256
     layer_type: linear
   - activation: relu
     layer_dim:
     - 256
     layer_type: linear
+env_cfg:
+  id: CartPole-v1
+  ignore_params:
+  - wrapper
+  - ignore_params
+  render_mode: null
+  wrapper: null

CartPole-v1/Train_single_CartPole-v1_DoubleDQN_20230516-114540/logs/log.txt ADDED Viewed

	@@ -0,0 +1,162 @@

+2023-05-16 11:45:40 - SimpleLog - INFO: - General Configs:
+2023-05-16 11:45:40 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:45:40 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:45:40 - SimpleLog - INFO: -       env_name      	        gym         	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -      algo_name      	     DoubleDQN      	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -         mode        	       train        	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -        device       	        cpu         	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -         seed        	         1          	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     max_episode     	        100         	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -       max_step      	        200         	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     collect_traj    	         0          	   <class 'bool'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -      mp_backend     	       single       	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -      n_workers      	         2          	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     online_eval     	         1          	   <class 'bool'>
+2023-05-16 11:45:40 - SimpleLog - INFO: - online_eval_episode 	         10         	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -    model_save_fre   	        500         	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -   load_checkpoint   	         0          	   <class 'bool'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -      load_path      	Train_CartPole-v1_DQN_20221026-054757	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -   load_model_step   	        best        	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:45:40 - SimpleLog - INFO: - Algo Configs:
+2023-05-16 11:45:40 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:45:40 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:45:40 - SimpleLog - INFO: -    epsilon_start    	        0.95        	  <class 'float'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     epsilon_end     	        0.01        	  <class 'float'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -    epsilon_decay    	        500         	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -        gamma        	        0.99        	  <class 'float'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -          lr         	       0.0001       	  <class 'float'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     buffer_size     	       100000       	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -      batch_size     	         64         	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -    target_update    	         4          	   <class 'int'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     value_layers    	[{'layer_type': 'linear', 'layer_dim': [256], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [256], 'activation': 'relu'}]	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     buffer_type     	     REPLAY_QUE     	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:45:40 - SimpleLog - INFO: - Env Configs:
+2023-05-16 11:45:40 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:45:40 - SimpleLog - INFO: -         Name        	       Value        	        Type
+2023-05-16 11:45:40 - SimpleLog - INFO: -          id         	    CartPole-v1     	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -     render_mode     	        None        	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -       wrapper       	        None        	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: -    ignore_params    	['wrapper', 'ignore_params']	   <class 'str'>
+2023-05-16 11:45:40 - SimpleLog - INFO: - ================================================================================
+2023-05-16 11:45:40 - SimpleLog - INFO: - obs_space: Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32), n_actions: Discrete(2)
+2023-05-16 11:45:40 - SimpleLog - INFO: - Start training!
+2023-05-16 11:45:40 - SimpleLog - INFO: - episode: 0, ep_reward: 25.0, ep_step: 25
+2023-05-16 11:45:40 - SimpleLog - INFO: - episode: 1, ep_reward: 17.0, ep_step: 17
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+2023-05-16 11:45:40 - SimpleLog - INFO: - episode: 18, ep_reward: 12.0, ep_step: 12
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 19, ep_reward: 27.0, ep_step: 27
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+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 23, ep_reward: 10.0, ep_step: 10
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 24, ep_reward: 15.0, ep_step: 15
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 25, ep_reward: 15.0, ep_step: 15
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+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 28, ep_reward: 10.0, ep_step: 10
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+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 31, ep_reward: 9.0, ep_step: 9
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 32, ep_reward: 11.0, ep_step: 11
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 33, ep_reward: 9.0, ep_step: 9
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 34, ep_reward: 13.0, ep_step: 13
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 35, ep_reward: 10.0, ep_step: 10
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 36, ep_reward: 9.0, ep_step: 9
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 37, ep_reward: 10.0, ep_step: 10
+2023-05-16 11:45:41 - SimpleLog - INFO: - update_step: 500, online_eval_reward: 9.000
+2023-05-16 11:45:41 - SimpleLog - INFO: - current update step obtain a better online_eval_reward: 9.000, save the best model!
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 38, ep_reward: 14.0, ep_step: 14
+2023-05-16 11:45:41 - SimpleLog - INFO: - episode: 39, ep_reward: 11.0, ep_step: 11
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+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 47, ep_reward: 30.0, ep_step: 30
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 48, ep_reward: 20.0, ep_step: 20
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 49, ep_reward: 28.0, ep_step: 28
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 50, ep_reward: 22.0, ep_step: 22
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 51, ep_reward: 20.0, ep_step: 20
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 52, ep_reward: 26.0, ep_step: 26
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 53, ep_reward: 24.0, ep_step: 24
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 54, ep_reward: 30.0, ep_step: 30
+2023-05-16 11:45:42 - SimpleLog - INFO: - episode: 55, ep_reward: 26.0, ep_step: 26
+2023-05-16 11:45:43 - SimpleLog - INFO: - episode: 56, ep_reward: 41.0, ep_step: 41
+2023-05-16 11:45:43 - SimpleLog - INFO: - episode: 57, ep_reward: 58.0, ep_step: 58
+2023-05-16 11:45:43 - SimpleLog - INFO: - episode: 58, ep_reward: 59.0, ep_step: 59
+2023-05-16 11:45:43 - SimpleLog - INFO: - update_step: 1000, online_eval_reward: 63.000
+2023-05-16 11:45:43 - SimpleLog - INFO: - current update step obtain a better online_eval_reward: 63.000, save the best model!
+2023-05-16 11:45:43 - SimpleLog - INFO: - episode: 59, ep_reward: 58.0, ep_step: 58
+2023-05-16 11:45:43 - SimpleLog - INFO: - episode: 60, ep_reward: 47.0, ep_step: 47
+2023-05-16 11:45:44 - SimpleLog - INFO: - episode: 61, ep_reward: 84.0, ep_step: 84
+2023-05-16 11:45:44 - SimpleLog - INFO: - episode: 62, ep_reward: 44.0, ep_step: 44
+2023-05-16 11:45:44 - SimpleLog - INFO: - episode: 63, ep_reward: 59.0, ep_step: 59
+2023-05-16 11:45:44 - SimpleLog - INFO: - episode: 64, ep_reward: 39.0, ep_step: 39
+2023-05-16 11:45:44 - SimpleLog - INFO: - episode: 65, ep_reward: 53.0, ep_step: 53
+2023-05-16 11:45:44 - SimpleLog - INFO: - episode: 66, ep_reward: 70.0, ep_step: 70
+2023-05-16 11:45:45 - SimpleLog - INFO: - episode: 67, ep_reward: 58.0, ep_step: 58
+2023-05-16 11:45:45 - SimpleLog - INFO: - update_step: 1500, online_eval_reward: 65.000
+2023-05-16 11:45:45 - SimpleLog - INFO: - current update step obtain a better online_eval_reward: 65.000, save the best model!
+2023-05-16 11:45:45 - SimpleLog - INFO: - episode: 68, ep_reward: 101.0, ep_step: 101
+2023-05-16 11:45:45 - SimpleLog - INFO: - episode: 69, ep_reward: 52.0, ep_step: 52
+2023-05-16 11:45:45 - SimpleLog - INFO: - episode: 70, ep_reward: 58.0, ep_step: 58
+2023-05-16 11:45:46 - SimpleLog - INFO: - episode: 71, ep_reward: 61.0, ep_step: 61
+2023-05-16 11:45:46 - SimpleLog - INFO: - episode: 72, ep_reward: 91.0, ep_step: 91
+2023-05-16 11:45:46 - SimpleLog - INFO: - episode: 73, ep_reward: 54.0, ep_step: 54
+2023-05-16 11:45:46 - SimpleLog - INFO: - update_step: 2000, online_eval_reward: 65.000
+2023-05-16 11:45:46 - SimpleLog - INFO: - episode: 74, ep_reward: 98.0, ep_step: 98
+2023-05-16 11:45:47 - SimpleLog - INFO: - episode: 75, ep_reward: 67.0, ep_step: 67
+2023-05-16 11:45:47 - SimpleLog - INFO: - episode: 76, ep_reward: 70.0, ep_step: 70
+2023-05-16 11:45:47 - SimpleLog - INFO: - episode: 77, ep_reward: 74.0, ep_step: 74
+2023-05-16 11:45:47 - SimpleLog - INFO: - episode: 78, ep_reward: 72.0, ep_step: 72
+2023-05-16 11:45:48 - SimpleLog - INFO: - episode: 79, ep_reward: 81.0, ep_step: 81
+2023-05-16 11:45:48 - SimpleLog - INFO: - episode: 80, ep_reward: 82.0, ep_step: 82
+2023-05-16 11:45:48 - SimpleLog - INFO: - update_step: 2500, online_eval_reward: 94.000
+2023-05-16 11:45:48 - SimpleLog - INFO: - current update step obtain a better online_eval_reward: 94.000, save the best model!
+2023-05-16 11:45:48 - SimpleLog - INFO: - episode: 81, ep_reward: 97.0, ep_step: 97
+2023-05-16 11:45:48 - SimpleLog - INFO: - episode: 82, ep_reward: 89.0, ep_step: 89
+2023-05-16 11:45:49 - SimpleLog - INFO: - episode: 83, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:50 - SimpleLog - INFO: - episode: 84, ep_reward: 142.0, ep_step: 142
+2023-05-16 11:45:50 - SimpleLog - INFO: - update_step: 3000, online_eval_reward: 153.000
+2023-05-16 11:45:50 - SimpleLog - INFO: - current update step obtain a better online_eval_reward: 153.000, save the best model!
+2023-05-16 11:45:50 - SimpleLog - INFO: - episode: 85, ep_reward: 114.0, ep_step: 114
+2023-05-16 11:45:51 - SimpleLog - INFO: - episode: 86, ep_reward: 162.0, ep_step: 162
+2023-05-16 11:45:51 - SimpleLog - INFO: - episode: 87, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:51 - SimpleLog - INFO: - update_step: 3500, online_eval_reward: 200.000
+2023-05-16 11:45:51 - SimpleLog - INFO: - current update step obtain a better online_eval_reward: 200.000, save the best model!
+2023-05-16 11:45:52 - SimpleLog - INFO: - episode: 88, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:53 - SimpleLog - INFO: - episode: 89, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:53 - SimpleLog - INFO: - update_step: 4000, online_eval_reward: 200.000
+2023-05-16 11:45:53 - SimpleLog - INFO: - episode: 90, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:54 - SimpleLog - INFO: - episode: 91, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:55 - SimpleLog - INFO: - episode: 92, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:55 - SimpleLog - INFO: - update_step: 4500, online_eval_reward: 200.000
+2023-05-16 11:45:55 - SimpleLog - INFO: - episode: 93, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:56 - SimpleLog - INFO: - episode: 94, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:57 - SimpleLog - INFO: - update_step: 5000, online_eval_reward: 200.000
+2023-05-16 11:45:57 - SimpleLog - INFO: - episode: 95, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:58 - SimpleLog - INFO: - episode: 96, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:58 - SimpleLog - INFO: - episode: 97, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:45:58 - SimpleLog - INFO: - update_step: 5500, online_eval_reward: 200.000
+2023-05-16 11:45:59 - SimpleLog - INFO: - episode: 98, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:46:00 - SimpleLog - INFO: - episode: 99, ep_reward: 200.0, ep_step: 200
+2023-05-16 11:46:00 - SimpleLog - INFO: - Finish training! total time consumed: 20.03s

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