readme: ---
  library_name: stable-baselines3
  tags:
  - SpaceInvadersNoFrameskip-v4
  - deep-reinforcement-learning
  - reinforcement-learning
  - stable-baselines3
  model-index:
  - name: DQN
    results:
    - task:
        type: reinforcement-learning
        name: reinforcement-learning
      dataset:
        name: SpaceInvadersNoFrameskip-v4
        type: SpaceInvadersNoFrameskip-v4
      metrics:
      - type: mean_reward
        value: 582.00 +/- 160.77
        name: mean_reward
        verified: false
  ---
  
  # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
  This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
  and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
  
  The RL Zoo is a training framework for Stable Baselines3
  reinforcement learning agents,
  with hyperparameter optimization and pre-trained agents included.
  
  ## Usage (with SB3 RL Zoo)
  
  RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
  SB3: https://github.com/DLR-RM/stable-baselines3<br/>
  SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
  
  ```
  # Download model and save it into the logs/ folder
  python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga loanb31 -f logs/
  python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4  -f logs/
  ```
  
  If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
  ```
  python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga loanb31 -f logs/
  rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4  -f logs/
  ```
  
  ## Training (with the RL Zoo)
  ```
  python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
  # Upload the model and generate video (when possible)
  python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga loanb31
  ```
  
  ## Hyperparameters
  ```python
  OrderedDict([('batch_size', 64),
               ('buffer_size', 100000),
               ('env_wrapper',
                ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
               ('exploration_final_eps', 0.01),
               ('exploration_fraction', 0.1),
               ('frame_stack', 4),
               ('gradient_steps', 1),
               ('learning_rate', 0.0001),
               ('learning_starts', 100000),
               ('n_timesteps', 10000000.0),
               ('optimize_memory_usage', False),
               ('policy', 'CnnPolicy'),
               ('target_update_interval', 1000),
               ('train_freq', 4),
               ('normalize', False)])
  ```
  