Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
33rd (2019)
Session ID : 1I3-J-2-01
Conference information

Development of Optimal Control Using AlphaZero Reinforcement Learning Algorithm
*Watabe MASAYAKun YANGDinesh MALLAKatsuyoshi SAKAMOTOKouichi YAMGUCHITomah SOGABE
Author information
CONFERENCE PROCEEDINGS FREE ACCESS

Details
Abstract

Deep Learning and Reinforcement Learning are developing rapidly in recent years. A lot of researches which apply deep reinforcement learning to the field such as game and robot control have generated great success. In this paper, we examine the possibility of adopting AlphaZero, an reinforcement learning algorithm demonstrates an unprecedented level of versatility for an game AI, to optimal control problems and gain insight on its ability to control the actions under noisy environment that is difficult to handle by using conventional control mechanism.

Content from these authors
© 2019 The Japanese Society for Artificial Intelligence
Previous article Next article
feedback
Top