Current
Reinforcement Learning for Traffic Signal Control
Deep and multi-agent reinforcement learning for intelligent traffic signal control, trained in rich 3D simulation and transferred to real-world networks.
Reinforcement Learning
Sequential decision making.
Projects
Publications
- When Memory Matters: An Evaluation of LSTM-Based Multi-Agent Learning for Multi-Intersection Traffic Signal ControlWhite Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2026
- Deep Reinforcement Learning Traffic Signal Controller for Large, Real-World Traffic NetworksWomen in engineering and science · 2025
- Synthesizing Traffic Datasets Using Graph Neural NetworksarXiv (Cornell University) · 2023
- Traffic3d: An Open-Source Traffic-Based Interactive Framework to Train Ai AgentsSSRN Electronic Journal · 2022
- Fully-Autonomous, Vision-based Traffic Signal Control: From Simulation to Reality2022
- Multi-Agent Deep Reinforcement Learning for Traffic optimization through Multiple Road Intersections using Live Camera Feed2020
- Traffic3D: A Rich 3D-Traffic Environment to Train Intelligent AgentsLecture notes in computer science · 2019
- A Deep Reinforcement Learning Agent for Traffic Intersection Control Optimization2019
- Traffic3D: A New Traffic Simulation Paradigm2019
- Deep Reinforcement Learning for Autonomous Traffic Light Control2018