15 citations · 35 across the 6 of their papers we have counts for
6 papers
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions
Qiyuan Liu, Qi Zhou, Rui Yang +1
Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, repres…
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Zhihai Wang, Xijun Li, Jie Wang +5
Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection -- which aim…
Efficient Exploration in Resource-Restricted Reinforcement Learning
Zhihai Wang, Taoxing Pan, Qi Zhou +1
In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode. Typical…
Learning to Reformulate for Linear Programming
Xijun Li, Qingyu Qu, Fangzhou Zhu +4
It has been verified that the linear programming (LP) is able to formulate many real-life optimization problems, which can obtain the optimum by resorting to corresponding solvers…
An Improved Reinforcement Learning Algorithm for Learning to Branch
Qingyu Qu, Xijun Li, Yunfan Zhou +6
Most combinatorial optimization problems can be formulated as mixed integer linear programming (MILP), in which branch-and-bound (B\&B) is a general and widely used method. Recentl…
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
Xijun Li, Weilin Luo, Mingxuan Yuan +5
The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling vehicles among multiple sites in order to minimize the cost when delivery orders are not known a p…