CycLight: learning traffic signal cooperation with a cycle-level strategy

• URL: http://arxiv.org/abs/2401.08121v1
• Date: Tue, 16 Jan 2024 05:28:12 GMT
• Title: CycLight: learning traffic signal cooperation with a cycle-level strategy
• Authors: Gengyue Han, Xiaohan Liu, Xianyue Peng, Hao Wang, Yu Han
• Abstract summary: This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Unlike most traditional RL-based traffic controllers that focus on step-by-step decision making, CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously.
• Score: 10.303270722832924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Unlike most traditional RL-based traffic controllers that focus on step-by-step decision making, CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm. This cycle-level approach effectively reduces the computational burden associated with frequent data communication, meanwhile enhancing the practicality and safety of real-world applications. A decentralized framework is formulated for multi-agent cooperation, while attention mechanism is integrated to accurately assess the impact of the surroundings on the current intersection. CycLight is tested in a large synthetic traffic grid using the microscopic traffic simulation tool, SUMO. Experimental results not only demonstrate the superiority of CycLight over other state-of-the-art approaches but also showcase its robustness against information transmission delays.

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