🤗 Model Zoo
CleanRL now has 🧪 experimental support for saving and loading models from 🤗 HuggingFace's Model Hub. We are rolling out this feature in phases, and currently only support saving and loading models from the following algorithm variants:
| Algorithm | Variants Implemented |
|---|---|
| ✅ Deep Q-Learning (DQN) | :material-github: dqn.py, :material-file-document: docs |
:material-github: dqn_atari.py, :material-file-document: docs |
|
:material-github: dqn_jax.py, :material-file-document: docs |
|
:material-github: dqn_atari_jax.py, :material-file-document: docs |
|
| ✅ Categorical DQN (C51) | :material-github: c51.py, :material-file-document: docs |
:material-github: c51_atari.py, :material-file-document: docs |
|
:material-github: c51_jax.py, :material-file-document: docs |
|
:material-github: c51_atari_jax.py, :material-file-document: docs |
|
| ✅ Deep Deterministic Policy Gradient (DDPG) | :material-github: ddpg_continuous_action.py, :material-file-document: docs |
:material-github: ddpg_continuous_action_jax.py, :material-file-document: docs |
|
| ✅ Twin Delayed Deep Deterministic Policy Gradient (TD3) | :material-github: td3_continuous_action.py, :material-file-document: docs |
:material-github: td3_continuous_action_jax.py, :material-file-document: docs |
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:
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 🤗 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.
<script src="https://emgithub.com/embed-v2.js?target=https%3A%2F%2Fgithub.com%2Fvwxyzjn%2Fcleanrl%2Fblob%2Fmaster%2Fcleanrl_utils%2Fevals%2Fdqn_eval.py&style=github&type=code&showBorder=on&showLineNumbers=on&showFileMeta=on&showFullPath=on&showCopy=on"></script>
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.
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