Initial commit
Browse files- README.md +11 -6
- args.yml +1 -1
- config.yml +2 -2
- dqn-SpaceInvadersNoFrameskip-v4.zip +2 -2
- dqn-SpaceInvadersNoFrameskip-v4/_stable_baselines3_version +1 -1
- dqn-SpaceInvadersNoFrameskip-v4/data +0 -0
- dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth +1 -1
- dqn-SpaceInvadersNoFrameskip-v4/policy.pth +1 -1
- dqn-SpaceInvadersNoFrameskip-v4/system_info.txt +4 -4
- replay.mp4 +2 -2
- results.json +1 -1
- train_eval_metrics.zip +2 -2
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
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type: SpaceInvadersNoFrameskip-v4
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metrics:
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- type: mean_reward
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-
value:
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name: mean_reward
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verified: false
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---
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@@ -36,21 +36,26 @@ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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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 numan966 -f logs/
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python enjoy
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```
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If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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```
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python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga numan966 -f logs/
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rl_zoo3
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```
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## Training (with the RL Zoo)
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```
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python train
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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 numan966
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```
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@@ -65,9 +70,9 @@ OrderedDict([('batch_size', 64),
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('exploration_fraction', 0.1),
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('frame_stack', 4),
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('gradient_steps', 1),
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-
('learning_rate', 0.
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('learning_starts', 100000),
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('n_timesteps',
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('optimize_memory_usage', False),
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('policy', 'CnnPolicy'),
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('target_update_interval', 1000),
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type: SpaceInvadersNoFrameskip-v4
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metrics:
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- type: mean_reward
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value: 618.00 +/- 169.87
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name: mean_reward
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verified: false
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---
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SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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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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# 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 numan966 -f logs/
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python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
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```
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If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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```
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python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga numan966 -f logs/
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python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
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```
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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 numan966
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```
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('exploration_fraction', 0.1),
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('frame_stack', 4),
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('gradient_steps', 1),
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('learning_rate', 0.0002),
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('learning_starts', 100000),
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('n_timesteps', 4000000.0),
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('optimize_memory_usage', False),
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('policy', 'CnnPolicy'),
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('target_update_interval', 1000),
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args.yml
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- - save_replay_buffer
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- false
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- - seed
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-
-
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- - storage
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- null
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- - study_name
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- - save_replay_buffer
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- false
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- - seed
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- 622850569
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- - storage
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- null
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- - study_name
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config.yml
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- - gradient_steps
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- 1
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- - learning_rate
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- 0.
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- - learning_starts
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- 100000
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- - n_timesteps
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-
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- - optimize_memory_usage
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- false
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- - policy
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- - gradient_steps
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- 1
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- - learning_rate
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- 0.0002
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- - learning_starts
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- 100000
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- - n_timesteps
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- 4000000.0
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- - optimize_memory_usage
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- false
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- - policy
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dqn-SpaceInvadersNoFrameskip-v4.zip
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dqn-SpaceInvadersNoFrameskip-v4/_stable_baselines3_version
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dqn-SpaceInvadersNoFrameskip-v4/data
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dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth
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dqn-SpaceInvadersNoFrameskip-v4/system_info.txt
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-
- OS: Linux-5.10.
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- Python: 3.8.
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- Stable-Baselines3: 1.7.
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- PyTorch: 1.13.
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- GPU Enabled: True
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- Numpy: 1.21.6
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- Gym: 0.21.0
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- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
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replay.mp4
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results.json
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{"mean_reward":
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train_eval_metrics.zip
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