A Deep Reinforcement Learning Approach to Adaptive Traffic Lights Management

Traffic monitoring and control, as well as traffic simulation, are still significant and open challenges despite the significant researches that have been carried out, especially on artificial intelligence approaches to tackle these problems. This paper presents a Reinforcement Learning approach to traffic lights control, coupled with a microscopic agent-based simulator (Simulation of Urban MObility SUMO) providing a synthetic but realistic environment in which the exploration of the outcome of potential regulation actions can be carried out. The paper presents the approach, within the current research landscape, then the specific experimental setting and achieved results are described.

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