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---
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library_name: stable-baselines3
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tags:
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- SpaceInvadersNoFrameskip-v4
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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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: 743.50 +/- 220.47
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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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This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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The RL Zoo is a training framework for Stable Baselines3
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reinforcement learning agents,
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with hyperparameter optimization and pre-trained agents included.
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## Usage (with SB3 RL Zoo)
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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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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 HusseinEid -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 HusseinEid -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 HusseinEid
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```
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## Hyperparameters
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```python
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OrderedDict([('batch_size', 32),
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('buffer_size', 100000),
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('env_wrapper',
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['stable_baselines3.common.atari_wrappers.AtariWrapper']),
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('exploration_final_eps', 0.01),
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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', 0.0001),
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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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('policy', 'CnnPolicy'),
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('target_update_interval', 1000),
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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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