Naslov
Combining Neural Gas and Reinforcement Learning for Adaptive Traffic Signal Control

Autori
Miletić, Mladen ; Ivanjko, Edouard ; Mandžuka, Sadko ; Koltovska-Nečoska, Daniela

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of ELMAR-2021 / Muštra, Mario ; Vuković, Josip ; Zovko-Cihlar, Branka - Zagreb : Hrvatsko društvo Elektronika u pomorstvu (ELMAR), 2021, 179-182

ISBN
978-1-6654-4436-1

Skup
63rd International Symposium ELMAR-2021

Mjesto i datum
Zadar, Hrvatska, 13.09.2021. - 15.09.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Intelligent Transportation Systems ; Adaptive Traffic Signal Control ; Reinforcement Learning ; Growing Neural Gas ; Machine Learning

Sažetak
Travel time of vehicles in urban traffic networks can be reduced by using Adaptive Traffic Signal Control (ATSC) to change the signal program according to the current traffic situation. Modern ATSC approaches based on Reinforcement Learning (RL) can learn the optimal signal control policy. While there are multiple RL based ATSC implementations available, most suffer from high state-action complexity leading to slow convergence and long training time. In this paper, the state-action complexity of ATSC based RL is reduced by implementing Growing Neural Gas learning structure as an integral part of RL, leading to high convergence rate and system stability. The presented approach is evaluated on a simulated signalized intersection, and compared with self-organizing map RL-based ATSC systems. Obtained results prove that the reduction of state-action complexity in this manner improves the effectiveness of RL based ATSC not needing to have an a priory analysis of needed number of neurons for state representation.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Tehnologija prometa i transport

POVEZANOST RADA