PokéLLMon：使用大型语言模型的精灵对战人类水平代理

摘要

我们介绍了Pok\'eLLMon，这是第一个在战术战斗游戏中达到人类水平性能的LLM实体代理，如在Pok\'emon战斗中所展示的。Pok\'eLLMon的设计融合了三种关键策略：(i) 上下文强化学习，即时利用从战斗中提取的基于文本的反馈来迭代地优化策略；(ii) 知识增强生成，检索外部知识以抵消幻觉，并使代理能够及时和正确地行动；(iii) 一致行动生成，以减轻代理面对强大对手时的惊慌切换现象，从而躲避战斗。我们展示了与人类的在线战斗，证明了Pok\'eLLMon的人类化战略和及时决策能力，其在梯队比赛中获胜率达到49％，在邀请战斗中获胜率达到56％。我们的实现和可玩战斗日志可在以下链接找到：https://github.com/git-disl/PokeLLMon。

English

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.

PokéLLMon：使用大型语言模型的精灵对战人类水平代理

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

摘要

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