LLM Agents Play Pokemon Showdown
A developer has created a multi-agent environment using Pokémon Showdown to test how Large Language Models (LLMs) collaborate and negotiate in a game-theory setting. The project aims to evaluate communication abilities between AI agents in a 2v2 competitive environment.
Why it matters
Understanding how AI agents coordinate and communicate is a critical step toward developing more sophisticated, autonomous multi-agent systems.
https://github.com/OliverGrabner/multi-agent-poke-showdown
The future is in LLM collaborations, and it is important to be able to understand how good agents are at working together, communicating and negotiating actions. Most multi-agent research is about workflows, where agents pass tasks down a pipeline ( Hong et al., 2024 ; Qian et al., 2024 ). To test this, I built a multi-agent environment on Pokémon Showdown's 2v2 format to evaluate these abilities.
"Both opps are Water types … so my Fire moves are resisted on both. … I plan to Wisp Dondozo … can you open with Fake Out to flinch one of them this turn?"
"Good call on Wisp-ing Dondozo. Quick correction though - Fighting isn't actually resisted by Water or Ground, so my Close Combat is neutral (1x) on both. … Locking in Fake Out → Whiscash."
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in