4 papers
Single-agent Reinforcement Learning Model for Regional Adaptive Traffic Signal Control
Qiang Li, Ningjing Zeng, Lina Yu
Several studies have employed reinforcement learning (RL) to address the challenges of regional adaptive traffic signal control (ATSC) and achieved promising results. In this field…
Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
Qiang Li, Jin Niu, Lina Yu
Traffic congestion, primarily driven by intersection queuing, significantly impacts urban living standards, safety, environmental quality, and economic efficiency. While Traffic Si…
Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning
Qiang Li, Jin Niu, Qin Luo +1
In the context of global urbanization and motorization, traffic congestion has become a significant issue, severely affecting the quality of life, environment, and economy. This pa…
DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance
Qiang Li, Yinhan Lin, Qin Luo +1
Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies. However, current RL algorithms necessitate excessive inte…