9 papers
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…
Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
Jihwan Jeong, Xiaoyu Wang, Jingmin Wang +2
Offline reinforcement learning (RL) is crucial when online exploration is costly or unsafe but often struggles with high epistemic uncertainty due to limited data. Existing methods…
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…
Path Planning for Air-Ground Robot Considering Modal Switching Point Optimization
Xiaoyu Wang, Kangyao Huang, Xinyu Zhang +5
An innovative sort of mobility platform that can both drive and fly is the air-ground robot. The need for an agile flight cannot be satisfied by traditional path planning technique…
A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control
Xiaoyu Wang, Scott Sanner, Baher Abdulhai
Recent years have witnessed substantial growth in adaptive traffic signal control (ATSC) methodologies that improve transportation network efficiency, especially in branches levera…
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…