Abstract
Adaptive traffic control systems (ATCS) can play an essential role in reducing traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement Learning (DRL) has been used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research. This research presents a comprehensive analysis of the widely used reward function. The pros and cons of various reward algorithms were discussed, and experimental analysis shows that the multi-objective reward function enhances the performance of ATSC.
DOI
10.17977/um018v4i22021p85-96
Recommended Citation
Jamil, Abu Rafe Md and Nower, Naushin
(2021)
"A Comprehensive Analysis of Reward Function for
Adaptive Traffic Signal Control,"
Knowledge Engineering and Data Science: Vol. 4:
No.
2, Article 2.
DOI: 10.17977/um018v4i22021p85-96
Available at:
https://citeus.um.ac.id/keds/vol4/iss2/2