A Comparison of Different State Representations for Reinforcement Learning Based Variable Speed Limit Control (CROSBI ID 663537)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Kušić, Krešimir ; Ivanjko, Edouard ; Gregurić, Martin
engleski
A Comparison of Different State Representations for Reinforcement Learning Based Variable Speed Limit Control
Variable Speed Limit Control (VSLC) is one control method for alleviating congestions on urban motorways. Machine learning techniques, like Reinforcement Learning (RL), are a promising alternative for setting up VSLC because an optimal control policy can be achieved with a smaller computational burden in comparison with optimal control approaches. A drawback is a large number of learning iterations and the problem of the exponential expansion of the state space dimension. This can be solved with function approximation techniques. Three different approaches for feature-based state representation in RL based VSLC are compared in this paper regarding the convergence of Total Time Spent. The microscopic traffic simulator VISSIM with a representative traffic model is used to evaluate the compared approaches. Results show that function approximation methods outperform RL based VSLC formulated with a lookup table by an average improvement of 10 %, where feature extraction methods (Coarse and Tile) coding showed slightly faster learning rate.
Intelligent transportation systems, Intelligent control systems, Variable speed limit control, Reinforcement learning, Tile coding, Coarse coding, RBF
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Podaci o prilogu
266-271.
2018.
objavljeno
10.1109/MED.2018.8442986
Podaci o matičnoj publikaciji
Proceedings of MED-2018
Mediterranean Control Association
978-1-5090-4532-7
Podaci o skupu
26th Mediterranean Conference on Control and Automation (MED 2018)
predavanje
19.06.2018-22.06.2018
Zadar, Hrvatska
Povezanost rada
Trošak objave rada u otvorenom pristupu
Elektrotehnika, Računarstvo, Tehnologija prometa i transport