Q-learning for single-agent and multi-agent and its application

Yiyang Hu; Gefei Yan

doi:10.1117/12.2642094

10 November 2022 Q-learning for single-agent and multi-agent and its application

Yiyang Hu, Gefei Yan

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123482D (2022) https://doi.org/10.1117/12.2642094
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Q-learning is a reinforcement learning method for solving Markov decision problems with incomplete information proposed by Watkins. With the development of reinforcement learning, more and more Q-learning related algorithms have been proposed, and their application range has become wider. In this paper, we discussed single agent algorithms including basic Q learning, deep Q learning and double Q learning. In addition, we discussed multi-agent algorithms including modular Q learning, ant Q learning and Nash Q learning with prominent characteristics. This paper will compare their advantages and disadvantages, and put forward our own views on the current application of Q-learning and the future trend of Q-learning.

Citation Download Citation

Yiyang Hu and Gefei Yan "Q-learning for single-agent and multi-agent and its application", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123482D (10 November 2022); https://doi.org/10.1117/12.2642094

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