Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

This work proposes the use of game-theoretic solutions and multi-
agent reinforcement learning in the mechanism design of smart and
sustainable mobility services. In particular, we focus on applications
to ridesharing as an example of a cooperative cost game. As such, we
firstly solve the coalition formation problem and propose algorithms
to allocate riders into cars in a socially optimal way. Secondly we
propose a mechanism to share the cost in an equitable way so
that ridesharing is incentivized. For the proposed methods, we
study properties of individual rationality and stability. Lastly, we
discuss future work, where we plan to compare centralized solutions
with decentralized algorithms based on multi-agent reinforcement
learning.
Original languageEnglish
Title of host publicationAAMAS'23
Pages3035
Number of pages2
ISBN (Electronic)9781450394321
Publication statusPublished - 29 May 2023

Publication series

NameAAMAS Conference proceedings
ISSN (Print)2523-5699

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