PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models

• URL: http://arxiv.org/abs/2402.01118v3
• Date: Tue, 2 Apr 2024 15:46:35 GMT
• Title: PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
• Authors: Sihao Hu, Tiansheng Huang, Ling Liu,
• Abstract summary: We introduce PokeLLMon, the first LLM-embodied agent that achieves human-parity performance in tactical battle games. We show that online battles against human demonstrates PokeLLMon's human-like battle strategies and just-in-time decision making.
• Score: 7.653580388741887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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