Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

By Ammar Haydari, Yasin Yilmaz. IEEE transactions on intelligent transportation systems (Print), 2020

625 citations recorded by the source corpus.

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.

Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/59aab88aba5115e6af18e7f0c32b692a17091c56

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8. Tao Li

Citations in the corpus

• A Review of Deep Reinforcement Learning for Smart Building Energy Management (2020, background): “, games [88] [31], autonomous driving [32]–[35], autonomous IoT [36], smart buildings [19] [20] [37] [38], smart city [39], wireless networks [40], Internet of energy [41], unmanned aerial vehicles [42], smart microgrids [43], edge computing [44], and manufacturing systems [45].”