WolfeClick

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.