A Study on Possibility of Predictive Deep Reinforcement Learners for Isolated Intersection Signal Control

DOI Web Site

Bibliographic Information

Other Title
  • 予測深層強化学習の単独交差点信号制御への適用性に関する一考察

Search this article

Description

<p>Reinforcement Learning (RL) methods have been introduced to traffic control application for several decades. Traditional RL-based signal controls are mostly model-free that ignore the complex traffic states variation. Such treatment is not realistic due to external uncertainty of traffic. To fill this gap, an independent prediction module could be introduced to formulate a model-based RL. This study introduces queuing estimation models into deep-Qnetwork(DQN)-based signal control. The queuing situation could be reproduced and predicted by both input-output model and shock wave model. Through the empirical experiment, we confirm the necessity of prediction in RL-based signal control for isolated intersection.</p>

Journal

  • SEISAN KENKYU

    SEISAN KENKYU 73 (2), 107-112, 2021-03-01

    Institute of Industrial Science The University of Tokyo

Details 詳細情報について

Report a problem

Back to top