Authors:
Ciprian Paduraru
;
Miruna Paduraru
and
Alin Stefanescu
Affiliation:
Department of Computer Science, University of Bucharest, Romania
Keyword(s):
Reinforcement Learning, Agent, Traffic Light Control, Optimization, Open-source Tool, Execution Management.
Abstract:
Traffic light control optimization is nowadays an important part of a smart city, given the advancement of sensors, IoT, and edge computing capabilities. The optimization method targeted by our work follows a general trend in the community: dynamically switching traffic light phases depending on the current traffic state. Reinforcement learning was lately adopted in the literature as it has been shown to outperform previous methods. The primary goal of our work is to provide an overview of the state of the art of reinforcement methods for traffic signal control optimization. Another topic of our work is to improve over existing tools that combine the field of reinforcement learning with traffic flow optimization. In this sense, we seek to add more output capabilities to existing tools to get closer to the domain-specific problem, to evaluate different algorithms for training strategies, to compare their performance and efficiency, and to simplify efforts in the research process by p
roviding ways to more easily capture and work with new data sets.
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