PokéLLMon: A Human-Parity Agent for Pokémon Battles with Large
  Language Models
Authors: name: Sihao Hu, name: Tiansheng Huang, name: Ling Liu

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.