A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges
arxiv(2024)
摘要
The swift evolution of Large-scale Models (LMs), either language-focused or
multi-modal, has garnered extensive attention in both academy and industry. But
despite the surge in interest in this rapidly evolving area, there are scarce
systematic reviews on their capabilities and potential in distinct impactful
scenarios. This paper endeavours to help bridge this gap, offering a thorough
examination of the current landscape of LM usage in regards to complex game
playing scenarios and the challenges still open. Here, we seek to
systematically review the existing architectures of LM-based Agents (LMAs) for
games and summarize their commonalities, challenges, and any other insights.
Furthermore, we present our perspective on promising future research avenues
for the advancement of LMs in games. We hope to assist researchers in gaining a
clear understanding of the field and to generate more interest in this highly
impactful research direction. A corresponding resource, continuously updated,
can be found in our GitHub repository.
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