Learning‐based control for discrete‐time constrained nonzero‐sum games

Learning‐based control for discrete‐time constrained nonzero‐sum games

Abstract A generalized policy‐iteration‐based solution to a class of discrete‐time multi‐player non‐zero‐sum games concerning the control constraints was proposed. Based on initial admissible control policies, the iterative value function of each player converges to the optimum approximately, which...

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Bibliographic Details
Main Authors: Chaoxu Mu, Jiangwen Peng, Yufei Tang
Format: Article
Language:English
Published: Wiley 2021-06-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
approximation theory
discrete time systems
dynamic programming
game theory
iterative methods
learning (artificial intelligence)
Online Access:https://doi.org/10.1049/cit2.12015

Internet

https://doi.org/10.1049/cit2.12015

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