Title: Multi-agent reinforcement learning-based approach for controlling signals through adaptation

Authors: Mohammed Tahifa; Jaouad Boumhidi; Ali Yahyaouy

Addresses: Informatics Department, Faculty of Science Dhar Mahraz (FSDM), Atlas-Fès, 30003, Morocco ' Informatics Department, Faculty of Science Dhar Mahraz (FSDM), Atlas-Fès, 30003, Morocco ' Informatics Department, Faculty of Science Dhar Mahraz (FSDM), Atlas-Fès, 30003, Morocco

Abstract: In this paper, we present a multi-agent reinforcement learning-based approach for controlling traffic signals. The aim is to use a multi-agent system with learning abilities for controlling and optimising traffic lights. We consider in this study the Q-learning algorithm, where the states are computed from average queue length in approaching links. The action space is modelled offline by using different time splits. The adaptation of the considered learning optimal policy through online learning is introduced to deal with the change of the environment. The simulation results show the effectiveness of the proposed adaptive learning algorithm.

Keywords: multi-agent systems; reinforcement learning; adaptation; traffic signal control; Q-learning.

DOI: 10.1504/IJAACS.2018.092019

International Journal of Autonomous and Adaptive Communications Systems, 2018 Vol.11 No.2, pp.129 - 143

Received: 28 Apr 2015
Accepted: 05 Oct 2015
Published online: 30 May 2018 *

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