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arxiv:2402.01118

PokéLLMon: A Human-Parity Agent for Pokémon Battles with Large Language Models

Published on Feb 2
· Submitted by akhaliq on Feb 5
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Abstract

We introduce 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 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 panic switching phenomenon when the agent faces a powerful opponent and wants to elude the battle. We show that online battles against human demonstrates 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: https://github.com/git-disl/PokeLLMon.

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Implementation can be found at: https://github.com/git-disl/PokeLLMon
Project website (battle animations): https://poke-llm-on.github.io

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PokéLLMon: The AI That Battles Like a Pro Pokémon Trainer!

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