6 papers · 1 filter
Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization
Yudong W. Xu, Wenhao Li, Xiaoyu Wang +2
Diffusion models have shown promise in learning to solve constraint optimization problems. However, they are mostly restricted to problems with binary variables and rely on graph n…
Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
Xiaocan Li, Xiaoyu Wang, Ilia Smirnov +2
Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading…
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
Xiaoyu Wang, Ayal Taitler, Scott Sanner +1
Efficient traffic signal control is essential for managing urban transportation, minimizing congestion, and improving safety and sustainability. Reinforcement Learning (RL) has eme…
Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control
Xiaocan Li, Xiaoyu Wang, Ilia Smirnov +2
Perimeter control (PC) prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous PC allows all access points to a protected region to have identical p…
Perimeter Control Using Deep Reinforcement Learning: A Model-free Approach towards Homogeneous Flow Rate Optimization
Xiaocan Li, Ray Coden Mercurius, Ayal Taitler +4
Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical v…
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb +3
Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy. Model-based approa…