PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
This addresses the challenge of developing AI agents that can compete with humans in complex, real-time strategic games, though it is incremental as it applies existing LLM techniques to a new domain.
The paper tackled the problem of creating an LLM-embodied agent for tactical battle games, specifically Pokemon battles, and achieved human-parity performance with win rates of 49% in Ladder competitions and 56% in invited battles.
We introduce PokeLLMon, the first LLM-embodied agent that achieves human-parity performance in tactical battle games, as demonstrated in Pokemon battles. The design of PokeLLMon incorporates three key strategies: (i) In-context reinforcement learning that instantly consumes text-based feedback derived from battles to iteratively refine the policy; (ii) Knowledge-augmented generation that retrieves external knowledge to counteract hallucination and enables the agent to act timely and properly; (iii) Consistent action generation to mitigate the panic switching phenomenon when the agent faces a powerful opponent and wants to elude the battle. We show that online battles against human demonstrates PokeLLMon's human-like battle strategies and just-in-time decision making, achieving 49% of win rate in the Ladder competitions and 56% of win rate in the invited battles. Our implementation and playable battle logs are available at: https://github.com/git-disl/PokeLLMon.