Newton's Method and Differential Dynamic Programming for Unconstrained Nonlinear Dynamic Games

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Dynamic games arise when multiple agents with differing objectives choose control inputs to a dynamic system. However, compared to single-agent control problems, the computational methods for dynamic games are relatively limited. Only very specialized dynamic games can be solved exactly, so approximation algorithms are required. In this paper, we show how to extend a recursive Newton algorithm and differential dynamic programming (DDP) to the case of full-information non-zero sum dynamic games. We show that the iterates of Newton's method and DDP are sufficiently close for DDP to inherit the quadratic convergence rate of Newton's method.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4073-4078
Number of pages6
ISBN (Electronic)9781728113982
DOIs
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2019-December
ISSN (Print)0743-1546

Conference

Conference58th IEEE Conference on Decision and Control, CDC 2019
CountryFrance
CityNice
Period12/11/1912/13/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

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