Initial commit
Browse files- README.md +27 -10
- args.yml +7 -1
- config.yml +1 -1
- 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 +2 -2
- dqn-SpaceInvadersNoFrameskip-v4/policy.pth +2 -2
- dqn-SpaceInvadersNoFrameskip-v4/pytorch_variables.pth +2 -2
- dqn-SpaceInvadersNoFrameskip-v4/system_info.txt +9 -7
- env_kwargs.yml +1 -1
- replay.mp4 +2 -2
- results.json +1 -1
- train_eval_metrics.zip +2 -2
README.md
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@@ -8,16 +8,17 @@ tags:
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model-index:
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- name: DQN
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results:
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-
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- type: mean_reward
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value: 599.00 +/- 222.51
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name: mean_reward
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task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: SpaceInvadersNoFrameskip-v4
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type: SpaceInvadersNoFrameskip-v4
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---
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# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
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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
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python enjoy
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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
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```
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## Hyperparameters
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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', 1000000.0),
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('optimize_memory_usage', False),
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('train_freq', 4),
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('normalize', False)])
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```
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model-index:
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- name: DQN
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: SpaceInvadersNoFrameskip-v4
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type: SpaceInvadersNoFrameskip-v4
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metrics:
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- type: mean_reward
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value: 443.50 +/- 142.69
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name: mean_reward
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verified: false
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---
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# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
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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 6001k1d -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 6001k1d -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 6001k1d
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```
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## Hyperparameters
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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', 0.0005),
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('learning_starts', 100000),
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('n_timesteps', 1000000.0),
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('optimize_memory_usage', False),
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('train_freq', 4),
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('normalize', False)])
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```
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# Environment Arguments
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```python
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{'render_mode': 'rgb_array'}
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```
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args.yml
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!!python/object/apply:collections.OrderedDict
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- - - algo
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- dqn
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- - device
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- auto
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- - env
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- null
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- - optimize_hyperparameters
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- - pruner
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- median
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- - sampler
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- - save_replay_buffer
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- false
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- sb3
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- - - algo
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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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- null
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- - optimize_hyperparameters
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- - progress
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- false
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- - pruner
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- null
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- sb3
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- - wandb_tags
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- []
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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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- - gradient_steps
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- 1
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- 0.0005
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- - learning_starts
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- 100000
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- - n_timesteps
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dqn-SpaceInvadersNoFrameskip-v4.zip
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dqn-SpaceInvadersNoFrameskip-v4/_stable_baselines3_version
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2.3.0a4
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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/pytorch_variables.pth
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dqn-SpaceInvadersNoFrameskip-v4/system_info.txt
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OS: Linux-
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Python: 3.
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Stable-Baselines3:
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PyTorch: 1.
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GPU Enabled: True
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Numpy: 1.
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- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
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- Python: 3.10.12
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- Stable-Baselines3: 2.3.0a4
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- PyTorch: 2.1.0+cu121
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- GPU Enabled: True
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- Numpy: 1.25.2
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- Cloudpickle: 2.2.1
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- Gymnasium: 0.29.1
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- OpenAI Gym: 0.26.2
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env_kwargs.yml
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render_mode: rgb_array
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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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