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dqn-SpaceInvadersNoFrameskip-v4

Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results
Model card Files Community
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Files changed (11) hide show
  1. README.md +86 -86
  2. args.yml +81 -81
  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 +2 -2
  7. dqn-SpaceInvadersNoFrameskip-v4/policy.pth +2 -2
  8. dqn-SpaceInvadersNoFrameskip-v4/system_info.txt +5 -5
  9. env_kwargs.yml +1 -1
  10. results.json +1 -1
  11. train_eval_metrics.zip +2 -2
README.md CHANGED
@@ -1,86 +1,86 @@
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: 284.50 +/- 147.47
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
-
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- The RL Zoo is a training framework for Stable Baselines3
30
- reinforcement learning agents,
31
- with hyperparameter optimization and pre-trained agents included.
32
-
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- ## Usage (with SB3 RL Zoo)
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-
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- 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
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-
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- Install the RL Zoo (with SB3 and SB3-Contrib):
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- ```bash
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- pip install rl_zoo3
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- ```
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-
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- ```
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- # Download model and save it into the logs/ folder
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- python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saintzeno -f logs/
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- python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
48
- ```
49
-
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- If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
51
- ```
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- python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saintzeno -f logs/
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- python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
54
- ```
55
-
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- ## Training (with the RL Zoo)
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- ```
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- python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
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- # Upload the model and generate video (when possible)
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- python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga saintzeno
61
- ```
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-
63
- ## Hyperparameters
64
- ```python
65
- OrderedDict([('batch_size', 48),
66
- ('buffer_size', 50000),
67
- ('env_wrapper',
68
- ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
69
- ('exploration_final_eps', 0.01),
70
- ('exploration_fraction', 0.1),
71
- ('frame_stack', 4),
72
- ('gradient_steps', 1),
73
- ('learning_rate', 0.001),
74
- ('learning_starts', 10000),
75
- ('n_timesteps', 1500000),
76
- ('optimize_memory_usage', False),
77
- ('policy', 'CnnPolicy'),
78
- ('target_update_interval', 1000),
79
- ('train_freq', 4),
80
- ('normalize', False)])
81
- ```
82
-
83
- # Environment Arguments
84
- ```python
85
- {'render_mode': 'rgb_array'}
86
- ```
 
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: 643.50 +/- 182.65
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
+
39
+ Install the RL Zoo (with SB3 and SB3-Contrib):
40
+ ```bash
41
+ pip install rl_zoo3
42
+ ```
43
+
44
+ ```
45
+ # Download model and save it into the logs/ folder
46
+ python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saintzeno -f logs/
47
+ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
48
+ ```
49
+
50
+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
51
+ ```
52
+ python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga saintzeno -f logs/
53
+ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
54
+ ```
55
+
56
+ ## Training (with the RL Zoo)
57
+ ```
58
+ python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
59
+ # Upload the model and generate video (when possible)
60
+ python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga saintzeno
61
+ ```
62
+
63
+ ## Hyperparameters
64
+ ```python
65
+ OrderedDict([('batch_size', 48),
66
+ ('buffer_size', 100000),
67
+ ('env_wrapper',
68
+ ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
69
+ ('exploration_final_eps', 0.01),
70
+ ('exploration_fraction', 0.1),
71
+ ('frame_stack', 4),
72
+ ('gradient_steps', 1),
73
+ ('learning_rate', 0.0001),
74
+ ('learning_starts', 50000),
75
+ ('n_timesteps', 2000000),
76
+ ('optimize_memory_usage', False),
77
+ ('policy', 'CnnPolicy'),
78
+ ('target_update_interval', 1000),
79
+ ('train_freq', 4),
80
+ ('normalize', False)])
81
+ ```
82
+
83
+ # Environment Arguments
84
+ ```python
85
+ {'render_mode': 'rgb_array'}
86
+ ```
args.yml CHANGED
@@ -1,81 +1,81 @@
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- !!python/object/apply:collections.OrderedDict
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- - - - algo
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- - - conf_file
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- - - eval_episodes
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- - - hyperparams
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- - logs/
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- - - wandb_entity
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+ - - hyperparams
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+ - false
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+ - - num_threads
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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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+ - tpe
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- - Python: 3.10.12
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  - Stable-Baselines3: 2.0.0
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- - PyTorch: 2.0.1+cu118
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- - GPU Enabled: True
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  - Cloudpickle: 2.2.1
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  - Gymnasium: 0.28.1
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  - OpenAI Gym: 0.26.2
 
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