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Application of Reinforcement Learning to Control Traffic Signals

Started ‎11-02-2021 by
Modified ‎01-14-2022 by
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Authors: Afshin Oroojlooy, Mohammadreza Nazari, Davood Hajinezhad, Jorge Silva

With the emergence of urbanization and the increase in household car ownership, traffic congestion has been one of the major challenges in many highly-populated cities. Traffic congestion can be mitigated by road expansion/correction, sophisticated road allowance rules, or improved traffic signal controlling. Although either of these solutions could decrease travel times and fuel costs, optimizing the traffic signals is more convenient due to limited funding resources and the opportunity of finding more effective strategies. Here we introduce a new framework for learning a general traffic control policy that can be deployed in an intersection of interest and ease its traffic flow.


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