--- tags: - CartPole-v1 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 438.60 +/- 87.15 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **CartPole-v1** This is a trained model of a DQN agent playing CartPole-v1. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[dqn]" python -m cleanrl_utils.enjoy --exp-name dqn --env-id CartPole-v1 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co./nsanghi/CartPole-v1-dqn-seed1/raw/main/dqn.py curl -OL https://huggingface.co./nsanghi/CartPole-v1-dqn-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co./nsanghi/CartPole-v1-dqn-seed1/raw/main/poetry.lock poetry install --all-extras python dqn.py --seed 1 --env-id CartPole-v1 --total-timesteps 50000 --track --capture-video --save-model --upload-model --hf-entity nsanghi ``` # Hyperparameters ```python {'batch_size': 128, 'buffer_size': 10000, 'capture_video': True, 'cuda': True, 'end_e': 0.05, 'env_id': 'CartPole-v1', 'exp_name': 'dqn', 'exploration_fraction': 0.5, 'gamma': 0.99, 'hf_entity': 'nsanghi', 'learning_rate': 0.00025, 'learning_starts': 10000, 'num_envs': 1, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 500, 'tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 50000, 'track': True, 'train_frequency': 10, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```