title: PokéLLMon: A Human-Parity Agent for Pokémon Battles with Large
    Language Models
summary: 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.
paper_authors._id: 65c04cc027a5fdca81ab6ee2
paper_authors.name: Sihao Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-05T12:10:10.659Z
paper_authors._id: 6539cab119c3ef6679794706
paper_authors.avatarUrl: /avatars/a88691ff5a547c7a1384edcc615c8209.svg
paper_authors.fullname: Sihao Hu
paper_authors.type: user
paper_authors.user: SihaoHu
paper_authors._id: 65c04cc027a5fdca81ab6ee3
paper_authors.name: Tiansheng Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:54:00.765Z
paper_authors._id: 67325283b318faa97f7ae5f7
paper_authors.avatarUrl: /avatars/2f83452768148b323c540c43ad695ee6.svg
paper_authors.fullname: TianshengHuang
paper_authors.type: user
paper_authors.user: TianshengHuang
paper_authors._id: 65c04cc027a5fdca81ab6ee4
paper_authors.name: Ling Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-03T08:17:07.850Z
paper_authors._id: 65c998005e17dbeaf147db84
paper_authors.avatarUrl: /avatars/6fb47b1e095971b93ff7dcd10369f926.svg
paper_authors.fullname: Ling Liu
paper_authors.type: user
paper_authors.user: ling1119
paper_title: PokéLLMon: A Human-Parity Agent for Pokémon Battles with Large
    Language Models
paper_summary: 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.
paper_ai_summary: Pok\'eLLMon, an LLM-embodied agent, achieves human-parity in tactical battle games through in-context reinforcement learning, knowledge-augmented generation, and consistent action generation, demonstrating human-like strategies and winning rates in various competitions.
paper_ai_keywords: LLM-embodied agent in-context reinforcement learning knowledge-augmented generation panic switching Ladder competitions