# ๐ค Model Zoo
[
](https://huggingface.co/cleanrl)
[](https://colab.research.google.com/github/vwxyzjn/cleanrl/blob/master/docs/get-started/CleanRL_Huggingface_Integration_Demo.ipynb)
CleanRL now has ๐งช experimental support for saving and loading models from ๐ค HuggingFace's [Model Hub](https://huggingface.co/models). We are rolling out this feature in phases, and currently only support saving and loading models from the following algorithm variants:
| Algorithm | Variants Implemented |
| ----------- | ----------- |
| โ
[Deep Q-Learning (DQN)](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf) | :material-github: [`dqn.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py), :material-file-document: [docs](/rl-algorithms/dqn/#dqnpy) |
| | :material-github: [`dqn_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py), :material-file-document: [docs](/rl-algorithms/dqn/#dqn_ataripy) |
| | :material-github: [`dqn_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_jax.py), :material-file-document: [docs](/rl-algorithms/dqn/#dqn_jaxpy) |
| | :material-github: [`dqn_atari_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari_jax.py), :material-file-document: [docs](/rl-algorithms/dqn/#dqn_atari_jaxpy) |
| โ
[Categorical DQN (C51)](https://arxiv.org/pdf/1707.06887.pdf) | :material-github: [`c51.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51.py), :material-file-document: [docs](/rl-algorithms/c51/#c51py) |
| | :material-github: [`c51_atari.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari.py), :material-file-document: [docs](/rl-algorithms/c51/#c51_ataripy) |
| | :material-github: [`c51_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_jax.py), :material-file-document: [docs](/rl-algorithms/c51/#c51_jaxpy) |
| | :material-github: [`c51_atari_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/c51_atari_jax.py), :material-file-document: [docs](/rl-algorithms/c51/#c51_atari_jaxpy) |
| โ
[Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/pdf/1509.02971.pdf) | :material-github: [`ddpg_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action.py), :material-file-document: [docs](/rl-algorithms/ddpg/#ddpg_continuous_actionpy) |
| | :material-github: [`ddpg_continuous_action_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ddpg_continuous_action_jax.py), :material-file-document: [docs](/rl-algorithms/ddpg/#ddpg_continuous_action_jaxpy)
| โ
[Twin Delayed Deep Deterministic Policy Gradient (TD3)](https://arxiv.org/pdf/1802.09477.pdf) | :material-github: [`td3_continuous_action.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action.py), :material-file-document: [docs](/rl-algorithms/td3/#td3_continuous_actionpy) |
| | :material-github: [`td3_continuous_action_jax.py`](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/td3_continuous_action_jax.py), :material-file-document: [docs](/rl-algorithms/td3/#td3_continuous_action_jaxpy) |
## Load models from the Model Hub
We have a simple utility `enjoy.py` to load models from the hub and run them in an environment. We currently support the following commands:
```bash
uv pip install ".[dqn]"
uv run python -m cleanrl_utils.enjoy --exp-name dqn --env-id CartPole-v1
uv pip install ".[dqn, jax]"
uv run python -m cleanrl_utils.enjoy --exp-name dqn_jax --env-id CartPole-v1
uv pip install ".[atari]"
uv run python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id BreakoutNoFrameskip-v4
uv pip install ".[atari, jax]"
uv run python -m cleanrl_utils.enjoy --exp-name dqn_atari_jax --env-id BreakoutNoFrameskip-v4
```
To see a list of supported models, please visit ๐ค [https://huggingface.co/cleanrl](https://huggingface.co/cleanrl).
???+ info "What happens under the hood?"
The `cleanrl_utils.enjoy` is a simple wrapper to load the models from the hub and run them in an environment. A minimal version of the script can be found at [cleanrl_utils/evals/dqn_eval.py](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl_utils/evals/dqn_eval.py), which may give you a more fine-grained control and access to the model.
## Save model to Model Hub
In the supported algorithm variants, you can run the script with the `--save-model` flag, which saves a model to the `runs` folder, and the `--upload-model` flag, which upload the model to huggingface under your default entity (username). Optionally, you may override the default entity with `--hf-entity` flag.
```bash
uv run python cleanrl/dqn_jax.py --env-id CartPole-v1 --save-model --upload-model # --hf-entity cleanrl
uv run python cleanrl/dqn_atari_jax.py --env-id SeaquestNoFrameskip-v4 --save-model --upload-model # --hf-entity cleanrl
```