Off-Policy Model-Free Learning for Multi-Player Non-Zero-Sum Games With Constrained Inputs-Reference-Cited by-同舟云学术

Off-Policy Model-Free Learning for Multi-Player Non-Zero-Sum Games With Constrained Inputs

Published:2023-02 Issue:2 Volume:70 Page:910-920
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Huo Yu¹^ORCID,Wang Ding¹^ORCID,Qiao Junfei¹^ORCID,Li Menghua¹^ORCID

Affiliation:

1. Beijing Key Laboratory of Computational Intelligence and Intelligent System and the Beijing Laboratory of Smart Environmental Protection, Faculty of Information Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China

Funder

National Key Research and Development Program of China

Beijing Natural Science Foundation

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/10027800/09952169.pdf?arnumber=9952169

Reference58 articles.

1. Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games