# WolfeClick

- **Event:** [OpenEnv Hackathon SF](https://cerebralvalley.ai/e/openenv-hackathon-sf)
- **When:** Mar 7 at 9:00 AM – Mar 8 at 12:00 AM (PST)
- **Where:** Shack15, San Francisco, CA
- **Team:** [Atharva Walawalkar](https://cerebralvalley.ai/u/ouchllama), [Aditya Bangde](https://cerebralvalley.ai/u/adityabangde)
- **GitHub:** https://github.com/Atharva2099/OpenEnv-WolfeClick
- **Website:** https://github.com/Atharva2099/OpenEnv-WolfeClick/blob/main/trainer.ipynb
- **Demo video:** https://youtu.be/xRd_2o_E7gw
- **Hugging Face:** https://huggingface.co/spaces/Atharva2099/WolfeClick?logs=container
- **Gallery:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/75

OpenEnv-WolfeClick is an OpenEnv-compatible environment for training LLMs in competitive Pokemon Showdown battles. Rock-paper-scissors already shows how cyclic matchups create nontrivial reasoning; Pokemon scales that into a much richer world with hidden information, many possible matchups, legal action constraints, and long-term consequences. The model must choose exactly one valid move or switch each turn from the live battle state.

We collect real rollout trajectories from battles and train a LoRA adapter with GRPO using actual environment reward, shaped by signals like damage, knockouts, setup, healing, status, and illegal-action penalties. The project fits Multi-Agent Interactions and Long-Horizon Planning, and demonstrates an end-to-end OpenEnv training loop for strategic decision-making under uncertainty.

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Markdown version of https://cerebralvalley.ai/e/openenv-hackathon-sf/hackathon/gallery/75. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt