Toll-Based Learning for Minimising Congestion under Heterogeneous Preferences

Onderzoeksoutput: Conference paper

3 Citaten (Scopus)
9 Downloads (Pure)

Samenvatting

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.

Originele taal-2English
TitelProceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2020
RedacteurenBo An, Amal El Fallah Seghrouchni, Gita Sukthankar
UitgeverijIFAAMAS
Pagina's1098-1106
Aantal pagina's9
ISBN van elektronische versie978-1-4503-7518-4
StatusPublished - 2020
EvenementThe 19th International Conference on Autonomous Agents and Multi-Agent Systems 2020 - Auckland, New Zealand
Duur: 9 mei 202013 mei 2020
https://aamas2020.conference.auckland.ac.nz

Publicatie series

NaamProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2020-May
ISSN van geprinte versie1548-8403
ISSN van elektronische versie1558-2914

Conference

ConferenceThe 19th International Conference on Autonomous Agents and Multi-Agent Systems 2020
Verkorte titelAAMAS 2020
LandNew Zealand
StadAuckland
Periode9/05/2013/05/20
Internet adres

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