johnjim0816's picture
update CartPole-v1 NoisyDQN
54d82df
2023-02-04 22:31:46 - r - INFO: - Hyperparameters:
2023-02-04 22:31:46 - r - INFO: - ================================================================================
2023-02-04 22:31:46 - r - INFO: - Name Value Type
2023-02-04 22:31:46 - r - INFO: - env_name CartPole-v1 <class 'str'>
2023-02-04 22:31:46 - r - INFO: - new_step_api 1 <class 'bool'>
2023-02-04 22:31:46 - r - INFO: - wrapper None <class 'str'>
2023-02-04 22:31:46 - r - INFO: - render 0 <class 'bool'>
2023-02-04 22:31:46 - r - INFO: - algo_name DRQN <class 'str'>
2023-02-04 22:31:46 - r - INFO: - mode train <class 'str'>
2023-02-04 22:31:46 - r - INFO: - seed 1 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - device cuda <class 'str'>
2023-02-04 22:31:46 - r - INFO: - train_eps 200 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - test_eps 10 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - eval_eps 10 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - eval_per_episode 5 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - max_steps 200 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - load_checkpoint 0 <class 'bool'>
2023-02-04 22:31:46 - r - INFO: - load_path Train_CartPole-v1_ <class 'str'>
2023-02-04 22:31:46 - r - INFO: - show_fig 0 <class 'bool'>
2023-02-04 22:31:46 - r - INFO: - save_fig 1 <class 'bool'>
2023-02-04 22:31:46 - r - INFO: - epsilon_start 0.1 <class 'float'>
2023-02-04 22:31:46 - r - INFO: - epsilon_end 0.001 <class 'float'>
2023-02-04 22:31:46 - r - INFO: - epsilon_decay 0.995 <class 'float'>
2023-02-04 22:31:46 - r - INFO: - hidden_dim 64 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - gamma 0.99 <class 'float'>
2023-02-04 22:31:46 - r - INFO: - lr 0.001 <class 'float'>
2023-02-04 22:31:46 - r - INFO: - buffer_size 100000 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - batch_size 8 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - target_update 4 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - lookup_step 10 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - min_epi_num 16 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - max_epi_len 100 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - max_epi_num 100 <class 'int'>
2023-02-04 22:31:46 - r - INFO: - value_layers [{'layer_type': 'linear', 'layer_dim': ['n_states', 64], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [64, 64], 'activation': 'relu'}, {'layer_type': 'linear', 'layer_dim': [64, 'n_actions'], 'activation': 'none'}] <class 'str'>
2023-02-04 22:31:46 - r - INFO: - task_dir /home/dingli/rl-tutorials/joyrl/tasks/Train_CartPole-v1_DRQN_20230204-223146 <class 'str'>
2023-02-04 22:31:46 - r - INFO: - model_dir /home/dingli/rl-tutorials/joyrl/tasks/Train_CartPole-v1_DRQN_20230204-223146/models <class 'str'>
2023-02-04 22:31:46 - r - INFO: - res_dir /home/dingli/rl-tutorials/joyrl/tasks/Train_CartPole-v1_DRQN_20230204-223146/results <class 'str'>
2023-02-04 22:31:46 - r - INFO: - log_dir /home/dingli/rl-tutorials/joyrl/tasks/Train_CartPole-v1_DRQN_20230204-223146/logs <class 'str'>
2023-02-04 22:31:46 - r - INFO: - traj_dir /home/dingli/rl-tutorials/joyrl/tasks/Train_CartPole-v1_DRQN_20230204-223146/traj <class 'str'>
2023-02-04 22:31:46 - r - INFO: - ================================================================================
2023-02-04 22:31:46 - r - INFO: - n_states: 4, n_actions: 2
2023-02-04 22:31:48 - r - INFO: - Start training!
2023-02-04 22:31:48 - r - INFO: - Env: CartPole-v1, Algorithm: DRQN, Device: cuda
2023-02-04 22:31:49 - r - INFO: - Episode: 1/200, Reward: 10.000, Step: 10
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2023-02-04 22:31:49 - r - INFO: - Episode: 5/200, Reward: 10.000, Step: 10
2023-02-04 22:31:49 - r - INFO: - Current episode 5 has the best eval reward: 10.000
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2023-02-04 22:32:46 - r - INFO: - Current episode 100 has the best eval reward: 118.000
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2023-02-04 22:33:16 - r - INFO: - Current episode 120 has the best eval reward: 181.000
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2023-02-04 22:35:37 - r - INFO: - Episode: 180/200, Reward: 200.000, Step: 200
2023-02-04 22:35:39 - r - INFO: - Current episode 180 has the best eval reward: 200.000
2023-02-04 22:35:42 - r - INFO: - Episode: 181/200, Reward: 200.000, Step: 200
2023-02-04 22:35:45 - r - INFO: - Episode: 182/200, Reward: 200.000, Step: 200
2023-02-04 22:35:47 - r - INFO: - Episode: 183/200, Reward: 200.000, Step: 200
2023-02-04 22:35:50 - r - INFO: - Episode: 184/200, Reward: 163.000, Step: 163
2023-02-04 22:35:52 - r - INFO: - Episode: 185/200, Reward: 200.000, Step: 200
2023-02-04 22:35:54 - r - INFO: - Current episode 185 has the best eval reward: 200.000
2023-02-04 22:35:57 - r - INFO: - Episode: 186/200, Reward: 200.000, Step: 200
2023-02-04 22:36:00 - r - INFO: - Episode: 187/200, Reward: 200.000, Step: 200
2023-02-04 22:36:02 - r - INFO: - Episode: 188/200, Reward: 200.000, Step: 200
2023-02-04 22:36:05 - r - INFO: - Episode: 189/200, Reward: 177.000, Step: 177
2023-02-04 22:36:07 - r - INFO: - Episode: 190/200, Reward: 159.000, Step: 159
2023-02-04 22:36:09 - r - INFO: - Current episode 190 has the best eval reward: 200.000
2023-02-04 22:36:12 - r - INFO: - Episode: 191/200, Reward: 200.000, Step: 200
2023-02-04 22:36:13 - r - INFO: - Episode: 192/200, Reward: 44.000, Step: 44
2023-02-04 22:36:15 - r - INFO: - Episode: 193/200, Reward: 200.000, Step: 200
2023-02-04 22:36:18 - r - INFO: - Episode: 194/200, Reward: 200.000, Step: 200
2023-02-04 22:36:21 - r - INFO: - Episode: 195/200, Reward: 200.000, Step: 200
2023-02-04 22:36:26 - r - INFO: - Episode: 196/200, Reward: 200.000, Step: 200
2023-02-04 22:36:28 - r - INFO: - Episode: 197/200, Reward: 199.000, Step: 199
2023-02-04 22:36:29 - r - INFO: - Episode: 198/200, Reward: 13.000, Step: 13
2023-02-04 22:36:29 - r - INFO: - Episode: 199/200, Reward: 15.000, Step: 15
2023-02-04 22:36:31 - r - INFO: - Episode: 200/200, Reward: 200.000, Step: 200
2023-02-04 22:36:33 - r - INFO: - Current episode 200 has the best eval reward: 200.000
2023-02-04 22:36:33 - r - INFO: - Finish training!