Can you catch ’em all? Meet PokéLLMon, the AI agent taking on human Pokémon players



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Summary

PokéLLMon is a language model-based AI agent that can beat humans at Pokémon.

PokéLLMon uses large language models, wiki entries, and a form of reinforcement learning to create an AI agent that is comparable to human players.

The Georgia Institute of Technology team sees the project as a test bed for developing agents that can behave like humans in virtual worlds. According to the team, tactical combat games, especially Pokémon Battles, provide a suitable format because they offer measurable victory rates, and consistent opponents, such as AI or human players, are always available.

Pokémon Battles are strategically challenging, requiring players to consider a wide range of factors, from the characteristics of the Pokémon to the environmental conditions of the game.

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PokéLLMon reads Pokédex and learns in battle

Without assistance, even the best language models, such as GPT-4, fall far short of the human level. So the team developed a method based on three key elements:

In-Context Reinforcement Learning (ICRL)

In ICRL, PokéLLMon iteratively improves its strategy based on text-based feedback from previous battles. This feedback serves as a kind of “reward” and includes information about the evolution of a Pokémon’s HP, the effectiveness of attacks, and the priority of move execution. According to the team, this allows the agent to continually refine its strategies and correct mistakes.

Knowledge Augmented Generation (KAG)

KAG allows PokéLLMon to incorporate external knowledge, such as type advantages and effects of moves or abilities, into its decision-making. This knowledge comes from the Pokédex, an encyclopedia of Pokémon. The team believes that the KAG reduces the problem of hallucinations.

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