Summary of Pokellmon: a Human-parity Agent For Pokemon Battles with Large Language Models, by Sihao Hu et al.
PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
by Sihao Hu, Tiansheng Huang, Ling Liu
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The PokeLLMon AI agent is a breakthrough in tactical battle games, achieving human-parity performance in Pokémon battles. This LLM-embodied agent utilizes three key strategies: in-context reinforcement learning, knowledge-augmented generation, and consistent action generation to refine its policy and counteract hallucination. By leveraging these strategies, PokeLLMon demonstrates human-like battle strategies and just-in-time decision making in online battles against humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PokeLLMon is a super cool AI agent that can play Pokémon games really well! It’s like a robot that learns how to win battles by reading feedback from the game. This AI has three special tricks: it gets smarter as it plays, it knows lots of things about Pokémon, and it makes good decisions quickly. PokeLLMon is so good that it can beat humans in battles! |
Keywords
» Artificial intelligence » Hallucination » Reinforcement learning
