26 citations · 73 across the 22 of their papers we have counts for
5 papers · 1 filter
Offline Reinforcement Learning with Adaptive Behavior Regularization
Yunfan Zhou, Xijun Li, Qingyu Qu
Offline reinforcement learning (RL) defines a sample-efficient learning paradigm, where a policy is learned from static and previously collected datasets without additional interac…
Machine Learning Methods in Solving the Boolean Satisfiability Problem
Wenxuan Guo, Junchi Yan, Hui-Ling Zhen +3
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques. Despi…
Yordle: An Efficient Imitation Learning for Branch and Bound
Qingyu Qu, Xijun Li, Yunfan Zhou
Combinatorial optimization problems have aroused extensive research interests due to its huge application potential. In practice, there are highly redundant patterns and characteri…
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…