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학술대회 An Agent-Based Simulation Modeling with Deep Reinforcement Learning for Smart Traffic Signal Control
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저자
장인국, 김동훈, 이동훈, 손영성
발행일
201810
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.1028-1030
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539377
협약과제
18ZH1100, 사물-사람-공간의 유기적 연결을 위한 초연결 공간의 분산 지능 핵심원천 기술, 손영성
초록
The traffic congestion in a city is one of the most important problems that must be taken into account in the smart city. Many cities suffer from the serious traffic congestion as the city population and the number of vehicles increase. To solve the traffic problem, reinforcement learning based research works have been studied for training models for traffic signal control agents and have shown their performance through various traffic simulation. The reason is that it is significantly difficult to acquire traffic data directly from the real-world environments. To effectively model reinforcement learning-based traffic simulations, in this paper, we study a method for integrating deep reinforcement learning into traffic simulation modeling.
키워드
Internet of Things, reinforcement learning, Simulation, traffic signal control
KSP 제안 키워드
Deep reinforcement learning, Internet of thing(IoT), Learning-based, Real-world, Reinforcement Learning(RL), Simulation modeling, Smart city, Smart traffic signal, Traffic Simulation, Traffic congestion, agent-based simulation