Generating Multi-agent Patrol Areas by Reinforcement Learning

Park, Bum-Jin; Kang, Cheongwoong; Choi, Jaesik

doi:10.23919/iccas52745.2021.9650047

Cited by 1 publication

(1 citation statement)

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“…The specific lane function and signal timing switching scheme are deployed, but the preplan must be repeatedly tested and the accuracy is not high. Literature [ 5 , 6 ] carried out integer nonlinear programming according to multiple road constraints associated with the target intersection. The optimization of the model achieves the smallest critical flow ratio after the optimization of a single intersection.…”

Section: Related Workmentioning

confidence: 99%

Advances in Computational Intelligence Techniques‐Based Multi‐Intersection Querying Theory for Efficient QoS in the Next Generation Internet of Things

Kumar

Kannan²,

Dahiya

et al. 2023

Computational Intelligence and Neuroscience

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An environment of physically linked, technologically networked things that can be found online is known as the “Internet of Things.” With the use of various devices connected to a network that allows data transfer between these devices, this includes the creation of intelligent communications and computational environments, such as intelligent homes, smart transportation systems, and intelligent FinTech. A variety of learning and optimization methods form the foundation of computational intelligence. Therefore, including new learning techniques such as opposition-based learning, optimization strategies, and reinforcement learning is the key growing trend for the next generation of IoT applications. In this study, a collaborative control system based on multiagent reinforcement learning with intelligent sensors for variable-guidance sections at various junctions is proposed. In the future generation of Internet of Things (IoT) applications, this study provides a multi-intersection variable steering lane-appropriate control approach that uses intelligent sensors to reduce traffic congestion at many junctions. Since the multi-intersection scene’s complicated traffic flow cannot be accommodated by the conventional variable steering lane management approach. The priority experience replay algorithm is also included to improve the efficiency of the transition sequence’s use in the experience replay pool and speed up the algorithm’s convergence for effective quality of service in the upcoming IoT applications. The experimental investigation demonstrates that the multi-intersection variable steering lane with intelligent sensors is an appropriate control mechanism, successfully reducing queue length and delay time. The effectiveness of waiting times and other indicators is superior to that of other control methods, which efficiently coordinate the strategy switching of variable steerable lanes and enhance the traffic capacity of the road network under multiple intersections for effective quality of service in the upcoming IoT applications.

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Section: Related Workmentioning

confidence: 99%