Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences

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

3 Citations (Scopus)
19 Downloads (Pure)

Abstract

Multiagent reinforcement learning has shown its potential for tackling real world problems, like traffic. We consider the toll-based route choice problem, where self-interested agents repeatedly commute attempting to minimise their travel costs. In this paper, we introduce Generalised Toll-based Q-learning (GTQ-learning), a multiagent reinforcement learning algorithm capable of realigning agents' heterogeneous preferences over travel time and monetary expenses to obtain a system-efficient equilibrium. GTQ-learning also includes a mechanism to enforce agents to truthfully report their preferences. Our theoretical analysis and empirical results show that GTQ-learning minimises congestion on realistic road networks.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020
EditorsBo An, Amal El Fallah Seghrouchni, Gita Sukthankar
PublisherIFAAMAS
Pages1098-1106
Number of pages9
ISBN (Electronic)978-1-4503-7518-4
Publication statusPublished - 2020
EventThe 19th International Conference on Autonomous Agents and Multi-Agent Systems 2020 - Auckland, New Zealand
Duration: 9 May 202013 May 2020
https://aamas2020.conference.auckland.ac.nz

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2020-May
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

ConferenceThe 19th International Conference on Autonomous Agents and Multi-Agent Systems 2020
Abbreviated titleAAMAS 2020
CountryNew Zealand
CityAuckland
Period9/05/2013/05/20
Internet address

Fingerprint

Dive into the research topics of 'Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences'. Together they form a unique fingerprint.

Cite this