Communicate with Traffic Lights and Vehicles Based on Multi-Agent Reinforcement Learning. - Qiang Wu 010; Peng Zhi; Yongqiang Wei; Liang Zhang 041; Jianqing Wu 002; Qingguo Zhou; Qiang Zhou; Pengfei Gao (CSCWD, 2021)

Efficient Pressure: Improving efficiency for signalized intersections. - Qiang Wu 010; Liang Zhang 041; Jun Shen 001; Linyuan Lü; Bo Du 004; Jianqing Wu 002 (CoRR, 2021)

Expression is enough: Improving traffic signal control with advanced traffic state representation. - Liang Zhang 041; Qiang Wu 010; Jun Shen 001; Linyuan Lü; Jianqing Wu 002; Bo Du 004 (CoRR, 2021)

Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control. - Liang Zhang 041; Qiang Wu 010; Jun Shen 001; Linyuan Lü; Bo Du 004; Jianqing Wu 002 (ICML, 2022)

Knowledge intensive state design for traffic signal control. - Liang Zhang 041; Qiang Wu 010; Jianming Deng (CoRR, 2022)

DynamicLight: Dynamically Tuning Traffic Signal Duration with DRL. - Liang Zhang 041; Qiang Wu 010; Jun Shen 001; Linyuan Lü; Bo Du 004; Akbar Telikani; Jianqing Wu 002; Shubin Xie (CoRR, 2022)

Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization. - Liang Zhang 041; Shubin Xie; Jianming Deng (ECML/PKDD (7), 2023)

Data Might be Enough: Bridge Real-World Traffic Signal Control Using Offline Reinforcement Learning. - Liang Zhang 041; Jianming Deng (CoRR, 2023)