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[](https://colab.research.google.com/github/meta-pytorch/OpenEnv/blob/main/examples/OpenEnv_Tutorial.ipynb)**← Try the Interactive Tutorial!**
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**🚀 Featured Example:** Train LLMs to play BlackJack using [torchforge](https://github.com/meta-pytorch/torchforge) (PyTorch's agentic RL framework): [`examples/grpo_blackjack/`](examples/grpo_blackjack/)
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## OpenEnv on partner platforms:
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-[Lightning AI Studio](https://lightning.ai/environments?section=featured)
-[Unsloth Google Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/OpenEnv_gpt_oss_(20B)_Reinforcement_Learning_2048_Game.ipynb)
OpenEnv provides a standard for interacting with agentic execution environments via simple Gymnasium style APIs - step(), reset(), state(). Users of agentic execution environments can interact with the environment during RL training loops using these simple APIs.
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- smolagents (for coding environment)
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## Supported RL Tools
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The goal of this project is to support a broad set of open and closed tools to help standardize the agentic RL community. If you have a project that supports OpenEnv environments, please put up a PR to add your tool name along with a link to your documentation.
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The goal of this project is to support a broad set of open and closed tools to help standardize the agentic RL community. If you have a project that supports OpenEnv environments, please put up a PR to add your tool name along with a link to your documentation.
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### torchforge
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(coming soon)
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See GRPO BlackJack training example: [`examples/grpo_blackjack/`](examples/grpo_blackjack/)
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### TRL
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(coming soon}
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See the [TRL example](https://huggingface.co/docs/trl/main/en/openenv) on how to integrate OpenEnv environments with GRPO training.
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### Unsloth
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(coming soon)
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See the 2048 game example based on gpt-oss: [Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/OpenEnv_gpt_oss_(20B)_Reinforcement_Learning_2048_Game.ipynb)
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### SkyRL
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(coming soon)
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See the [SkyRL example](https://skyrl.readthedocs.io/en/latest/examples/openenv.html) on how to train on OpenEnv environments with SkyRL.
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### ART
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See the [ART example](https://art.openpipe.ai/integrations/openenv-integration) on how OpenEnv environments can be used to train models with ART.
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## Example Environments
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And we'd also like to acknowledge the team at Farama Foundation as the OpenEnv API was heavily inspired by the work you all have done on Gymnasium. Cheers!
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