PokéLLMon: A Human-Parity Agent for Pokémon Battles with Large
  Language Models
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.
65c04cc027a5fdca81ab6ee2
Sihao Hu
admin_assigned
2024-02-05T12:10:10.659Z
6539cab119c3ef6679794706
/avatars/a88691ff5a547c7a1384edcc615c8209.svg
Sihao Hu
user
SihaoHu
65c04cc027a5fdca81ab6ee3
Tiansheng Huang
claimed_verified
2024-11-15T09:54:00.765Z
67325283b318faa97f7ae5f7
/avatars/2f83452768148b323c540c43ad695ee6.svg
TianshengHuang
user
TianshengHuang
65c04cc027a5fdca81ab6ee4
Ling Liu
claimed_verified
2025-02-03T08:17:07.850Z
65c998005e17dbeaf147db84
/avatars/6fb47b1e095971b93ff7dcd10369f926.svg
Ling Liu
user
ling1119
PokéLLMon: A Human-Parity Agent for Pokémon Battles with Large
  Language Models
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.
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.
LLM-embodied agent in-context reinforcement learning knowledge-augmented generation panic switching Ladder competitions