README.md

metadata

tags:
  - PongNoFrameskip-v4
  - deep-reinforcement-learning
  - reinforcement-learning
  - custom-implementation
library_name: cleanrl
model-index:
  - name: DQN
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: PongNoFrameskip-v4
          type: PongNoFrameskip-v4
        metrics:
          - type: mean_reward
            value: 18.80 +/- 1.25
            name: mean_reward
            verified: false

(CleanRL) DQN Agent Playing PongNoFrameskip-v4

This is a trained model of a DQN agent playing PongNoFrameskip-v4. The model was trained by using CleanRL and the most up-to-date training code can be found here.

Get Started

To use this model, please install the cleanrl package with the following command:

pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id PongNoFrameskip-v4

Please refer to the documentation for more detail.

Command to reproduce the training

curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/odiaz1066/PongNoFrameskip-v4-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --env-id PongNoFrameskip-v4 --track --save-model --capture-video --resume --total-timesteps 0 --upload-model --seed 1 --hf-entity odiaz1066

Hyperparameters

{'batch_size': 32,
 'buffer_size': 1000000,
 'capture_video': True,
 'checkpoint': False,
 'checkpoint_frequency': 100000,
 'cuda': True,
 'end_e': 0.01,
 'env_id': 'PongNoFrameskip-v4',
 'exp_name': 'dqn_atari',
 'exploration_fraction': 0.1,
 'gamma': 0.99,
 'hf_entity': 'odiaz1066',
 'initial_steps': 0,
 'learning_rate': 0.0001,
 'learning_starts': 80000,
 'num_envs': 1,
 'resume': True,
 'rotate': False,
 'save_model': True,
 'seed': 1,
 'start_e': 1,
 'target_network_frequency': 1000,
 'tau': 1.0,
 'torch_deterministic': True,
 'total_timesteps': 0,
 'track': True,
 'train_frequency': 4,
 'upload_model': True,
 'wandb_entity': None,
 'wandb_project_name': 'lagomorph'}