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.
Vidali, A., Crociani, L., Vizzari, G., Bandini, S. (2019). A deep reinforcement learning approach to adaptive traffic lights management. In 20th Workshop "From Objects to Agents", WOA 2019 (pp.42-50). CEUR-WS.
A deep reinforcement learning approach to adaptive traffic lights management
Crociani L.;Vizzari G.;Bandini S.
2019
Abstract
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
