--- tags: - Swimmer-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: TD3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Swimmer-v4 type: Swimmer-v4 metrics: - type: mean_reward value: 80.59 +/- 12.45 name: mean_reward verified: false --- # (CleanRL) **TD3** Agent Playing **Swimmer-v4** This is a trained model of a TD3 agent playing Swimmer-v4. 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/td3_continuous_action.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[td3_continuous_action]" python -m cleanrl_utils.enjoy --exp-name td3_continuous_action --env-id Swimmer-v4 ``` 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/sdpkjc/Swimmer-v4-td3_continuous_action-seed5/raw/main/td3_continuous_action.py curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-td3_continuous_action-seed5/raw/main/pyproject.toml curl -OL https://huggingface.co/sdpkjc/Swimmer-v4-td3_continuous_action-seed5/raw/main/poetry.lock poetry install --all-extras python td3_continuous_action.py --save-model --upload-model --hf-entity sdpkjc --env-id Swimmer-v4 --seed 5 --track ``` # Hyperparameters ```python {'batch_size': 256, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'env_id': 'Swimmer-v4', 'exp_name': 'td3_continuous_action', 'exploration_noise': 0.1, 'gamma': 0.99, 'hf_entity': 'sdpkjc', 'learning_rate': 0.0003, 'learning_starts': 25000.0, 'noise_clip': 0.5, 'policy_frequency': 2, 'policy_noise': 0.2, 'save_model': True, 'seed': 5, 'tau': 0.005, 'torch_deterministic': True, 'total_timesteps': 1000000, 'track': True, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```