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LeonardoMdSA
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SpaceInvadersNoFrameskip-v4

Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Model card Files
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Files changed (12) hide show
  1. README.md +87 -87
  2. args.yml +85 -85
  3. config.yml +29 -29
  4. dqn-SpaceInvadersNoFrameskip-v4.zip +2 -2
  5. dqn-SpaceInvadersNoFrameskip-v4/data +0 -0
  6. dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth +1 -1
  7. dqn-SpaceInvadersNoFrameskip-v4/policy.pth +1 -1
  8. dqn-SpaceInvadersNoFrameskip-v4/pytorch_variables.pth +1 -1
  9. dqn-SpaceInvadersNoFrameskip-v4/system_info.txt +5 -5
  10. env_kwargs.yml +1 -1
  11. results.json +1 -1
  12. train_eval_metrics.zip +2 -2
README.md CHANGED
@@ -1,87 +1,87 @@
1
- ---
2
- library_name: stable-baselines3
3
- tags:
4
- - SpaceInvadersNoFrameskip-v4
5
- - deep-reinforcement-learning
6
- - reinforcement-learning
7
- - stable-baselines3
8
- model-index:
9
- - name: DQN
10
- results:
11
- - task:
12
- type: reinforcement-learning
13
- name: reinforcement-learning
14
- dataset:
15
- name: SpaceInvadersNoFrameskip-v4
16
- type: SpaceInvadersNoFrameskip-v4
17
- metrics:
18
- - type: mean_reward
19
- value: 207.00 +/- 190.99
20
- name: mean_reward
21
- verified: false
22
- ---
23
-
24
- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
25
- This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
26
- using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
27
- and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
28
-
29
- The RL Zoo is a training framework for Stable Baselines3
30
- reinforcement learning agents,
31
- with hyperparameter optimization and pre-trained agents included.
32
-
33
- ## Usage (with SB3 RL Zoo)
34
-
35
- RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
36
- SB3: https://github.com/DLR-RM/stable-baselines3<br/>
37
- SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
38
- SBX (SB3 + Jax): https://github.com/araffin/sbx
39
-
40
- Install the RL Zoo (with SB3 and SB3-Contrib):
41
- ```bash
42
- pip install rl_zoo3
43
- ```
44
-
45
- ```
46
- # Download model and save it into the logs/ folder
47
- python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga LeonardoMdSA -f logs/
48
- python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
49
- ```
50
-
51
- If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
52
- ```
53
- python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga LeonardoMdSA -f logs/
54
- python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
55
- ```
56
-
57
- ## Training (with the RL Zoo)
58
- ```
59
- python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
60
- # Upload the model and generate video (when possible)
61
- python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga LeonardoMdSA
62
- ```
63
-
64
- ## Hyperparameters
65
- ```python
66
- OrderedDict([('batch_size', 32),
67
- ('buffer_size', 50000),
68
- ('env_wrapper',
69
- ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
70
- ('exploration_final_eps', 0.01),
71
- ('exploration_fraction', 0.05),
72
- ('frame_stack', 4),
73
- ('gradient_steps', 4),
74
- ('learning_rate', 0.0005),
75
- ('learning_starts', 20000),
76
- ('n_timesteps', 500000.0),
77
- ('optimize_memory_usage', False),
78
- ('policy', 'CnnPolicy'),
79
- ('target_update_interval', 1000),
80
- ('train_freq', 4),
81
- ('normalize', False)])
82
- ```
83
-
84
- # Environment Arguments
85
- ```python
86
- {'render_mode': 'rgb_array'}
87
- ```
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - SpaceInvadersNoFrameskip-v4
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: DQN
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: SpaceInvadersNoFrameskip-v4
16
+ type: SpaceInvadersNoFrameskip-v4
17
+ metrics:
18
+ - type: mean_reward
19
+ value: 744.50 +/- 249.69
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
25
+ This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
27
+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
28
+
29
+ The RL Zoo is a training framework for Stable Baselines3
30
+ reinforcement learning agents,
31
+ with hyperparameter optimization and pre-trained agents included.
32
+
33
+ ## Usage (with SB3 RL Zoo)
34
+
35
+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
36
+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
37
+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
38
+ SBX (SB3 + Jax): https://github.com/araffin/sbx
39
+
40
+ Install the RL Zoo (with SB3 and SB3-Contrib):
41
+ ```bash
42
+ pip install rl_zoo3
43
+ ```
44
+
45
+ ```
46
+ # Download model and save it into the logs/ folder
47
+ python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga LeonardoMdSA -f logs/
48
+ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
49
+ ```
50
+
51
+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
52
+ ```
53
+ python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga LeonardoMdSA -f logs/
54
+ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
55
+ ```
56
+
57
+ ## Training (with the RL Zoo)
58
+ ```
59
+ python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
60
+ # Upload the model and generate video (when possible)
61
+ python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga LeonardoMdSA
62
+ ```
63
+
64
+ ## Hyperparameters
65
+ ```python
66
+ OrderedDict([('batch_size', 32),
67
+ ('buffer_size', 100000),
68
+ ('env_wrapper',
69
+ ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
70
+ ('exploration_final_eps', 0.01),
71
+ ('exploration_fraction', 0.1),
72
+ ('frame_stack', 4),
73
+ ('gradient_steps', 1),
74
+ ('learning_rate', 0.0001),
75
+ ('learning_starts', 100000),
76
+ ('n_timesteps', 1000000.0),
77
+ ('optimize_memory_usage', False),
78
+ ('policy', 'CnnPolicy'),
79
+ ('target_update_interval', 1000),
80
+ ('train_freq', 4),
81
+ ('normalize', False)])
82
+ ```
83
+
84
+ # Environment Arguments
85
+ ```python
86
+ {'render_mode': 'rgb_array'}
87
+ ```
args.yml CHANGED
@@ -1,85 +1,85 @@
1
- !!python/object/apply:collections.OrderedDict
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- - - - algo
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- - dqn
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- - - conf_file
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- - dqn.yml
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- - - n_trials
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- - - truncate_last_trajectory
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- - - wandb_entity
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- - null
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- - - wandb_project_name
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- - sb3
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- - - wandb_tags
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- - []
 
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+ - - - algo
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+ - dqn
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+ - - conf_file
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+ - dqn.yml
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+ - - device
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+ - auto
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+ - - env
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+ - SpaceInvadersNoFrameskip-v4
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+ - - env_kwargs
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+ - null
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+ - - eval_env_kwargs
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+ - null
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+ - 25000
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+ - - gym_packages
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+ - - hyperparams
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+ - null
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+ - - log_folder
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+ - ./logs
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+ - -1
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+ - - n_trials
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+ - 500
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+ - - no_optim_plots
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+ - false
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+ - -1
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+ - - optimization_log_path
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+ - null
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+ - - optimize_hyperparameters
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+ - false
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+ - median
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+ - 622647416
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+ - ''
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+ - - truncate_last_trajectory
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+ - true
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+ - false
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- - CnnPolicy
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- - - train_freq
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- - OS: Windows-11-10.0.22621-SP0 10.0.22621
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  - Stable-Baselines3: 2.8.0a2
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- - PyTorch: 2.9.1+cpu
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  - Cloudpickle: 3.1.2
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  - OpenAI Gym: 0.25.2
 
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