Pok\'eLLMon: A Human-Parity Agent for Pok\'emon Battles with Large Language Models

Sihao Hu, Tiansheng Huang, Ling Liu
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Computation and Language (cs.CL)
2024-02-02 00:00:00
We introduce \textsc{Pok\'eLLMon}, the first LLM-embodied agent that achieves human-parity performance in tactical battle games, as demonstrated in Pok\'emon battles. The design of \textsc{Pok\'eLLMon} 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 \textit{panic switching} phenomenon when the agent faces a powerful opponent and wants to elude the battle. We show that online battles against human demonstrates \textsc{Pok\'eLLMon}'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: \url{}.
PDF: Pok\'eLLMon: A Human-Parity Agent for Pok\'emon Battles with Large Language Models.pdf
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