Roadmap of AlphaGo to AlphaStar: Problems and challenges

Yuhan Rong

doi:10.1117/12.2641824

10 November 2022 Roadmap of AlphaGo to AlphaStar: Problems and challenges

Yuhan Rong

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123483D (2022) https://doi.org/10.1117/12.2641824
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

In recent years, deep reinforcement learning is one of the hot topics of artificial intelligence, which can be applied in many fields. However, it also faces many problems and challenges, such as insufficient sample data, large sample space, complex action space and so on. The emergence of AlphaGo solved the problem of large sample space very well. After that, artificial intelligence such as AlphaGo Zero and AlphaStar were released. These intelligent frameworks can be applied in various scenarios. Differentiating from other works, in this paper, we focus on the in-depth analysis of the internal connections of Alpha series from the perspective of the problems and challenges solved to give an insight for the future development of reinforcement learning.

Citation Download Citation

Yuhan Rong "Roadmap of AlphaGo to AlphaStar: Problems and challenges", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123483D (10 November 2022); https://doi.org/10.1117/12.2641824

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