Multi-agent reinforcement learning-based approach for controlling signals through adaptation
by Mohammed Tahifa; Jaouad Boumhidi; Ali Yahyaouy
International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Vol. 11, No. 2, 2018

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.

Online publication date: Wed, 30-May-2018

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