PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
<|reference_start|>PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models: We introduce PokeLLMon, the first LLM-embodied agent that achieves human-parity performance in tactical battle games, as demonstrated in Pokemon battles. The design of PokeLLMon 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 PokeLLMon'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.<|reference_end|>
@article{hu2024pokellmon:,
    title={PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language
  Models},
    author={Sihao Hu, Tiansheng Huang, Ling Liu},
    journal={arXiv preprint arXiv:2402.01118},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01118},
    primaryClass={cs.AI cs.CL}
}