--- tags: - Hopper-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DDPG results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Hopper-v4 type: Hopper-v4 metrics: - type: mean_reward value: 1675.53 +/- 1038.51 name: mean_reward verified: false --- # (CleanRL) **DDPG** Agent Playing **Hopper-v4** This is a trained model of a DDPG agent playing Hopper-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/ddpg_continuous_action.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[ddpg_continuous_action]" python -m cleanrl_utils.enjoy --exp-name ddpg_continuous_action --env-id Hopper-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/cleanrl/Hopper-v4-ddpg_continuous_action-seed1/raw/main/ddpg_continuous_action.py curl -OL https://huggingface.co/cleanrl/Hopper-v4-ddpg_continuous_action-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/cleanrl/Hopper-v4-ddpg_continuous_action-seed1/raw/main/poetry.lock poetry install --all-extras python ddpg_continuous_action.py --track --capture-video --save-model --hf-entity cleanrl --upload-model --env-id Hopper-v4 --seed 1 ``` # Hyperparameters ```python {'batch_size': 256, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'env_id': 'Hopper-v4', 'exp_name': 'ddpg_continuous_action', 'exploration_noise': 0.1, 'gamma': 0.99, 'hf_entity': 'cleanrl', 'learning_rate': 0.0003, 'learning_starts': 25000.0, 'noise_clip': 0.5, 'policy_frequency': 2, 'save_model': True, 'seed': 1, 'tau': 0.005, 'torch_deterministic': True, 'total_timesteps': 1000000, 'track': True, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```