Probabilistic Graph Models: A Key to Boosting Deep Reinforcement Learning in Urban Traffic Networks

Sattarzadeh, Ali Reza; Pathirana, Pubudu

Probabilistic Graph Models: A Key to Boosting Deep Reinforcement Learning in Urban Traffic Networks

conference contribution

posted on 2025-05-26, 03:31 authored by Ali Reza Sattarzadeh, Pubudu PathiranaPubudu Pathirana

Probabilistic Graph Models: A Key to Boosting Deep Reinforcement Learning in Urban Traffic Networks

History

Volume

00

Pagination

201-207

Location

Perth, W.A.

Open access

No

Start date

2025-03-20

End date

2025-03-22

ISSN

2154-4352

eISSN

2154-4360

ISBN-13

979-8-3315-3381-6

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

ICCAE 2025 : Proceedings of the 17th International Conference on Computer and Automation Engineering 2025

Event

Computer and Automation Engineering. Conference (17th : 2025 : Perth, W.A.)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Place of publication

Piscataway, N.J.

Usage metrics

Keywords

11 Sustainable Cities and Communities Probabilistic Graph Models (PGMs)Deep Reinforcement Learning (DRL)intelligent traffic management smart city infrastructure

Probabilistic Graph Models: A Key to Boosting Deep Reinforcement Learning in Urban Traffic Networks

History

Volume

Pagination

Location

Open access

Start date

End date

ISSN

eISSN

ISBN-13

Language

Publication classification

Title of proceedings

Event

Publisher

Place of publication

Usage metrics

Categories

Keywords

Licence

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