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: https://github.com/git-disl/PokeLLMon.

通过人类与 Pokémon 战斗中表现出的在线对战策略和即时决策，我们展示了 PokéLLMon 在战术战斗游戏中达到了与人类相似的表现，并取得了梯队赛中 49% 获胜率和邀请赛中 56% 获胜率，实现和可播放的战斗记录可在 https://github.com/git-disl/PokeLLMon 获取。