Combining Neural Gas and Reinforcement Learning for Adaptive Traffic Signal Control (CROSBI ID 706729)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Miletić, Mladen ; Ivanjko, Edouard ; Mandžuka, Sadko ; Koltovska-Nečoska, Daniela
engleski
Combining Neural Gas and Reinforcement Learning for Adaptive Traffic Signal Control
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.
Intelligent Transportation Systems ; Adaptive Traffic Signal Control ; Reinforcement Learning ; Growing Neural Gas ; Machine Learning
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Podaci o prilogu
179-182.
2021.
objavljeno
10.1109/ELMAR52657.2021.9550948
Podaci o matičnoj publikaciji
Proceedings of ELMAR-2021
Muštra, Mario ; Vuković, Josip ; Zovko-Cihlar, Branka
Zagreb: Hrvatsko društvo Elektronika u pomorstvu (ELMAR)
978-1-6654-4436-1
Podaci o skupu
63rd International Symposium ELMAR 2021
predavanje
12.09.2021-15.09.2021
Zadar, Hrvatska
Povezanost rada
Elektrotehnika, Računarstvo, Tehnologija prometa i transport