1 code implementation • 31 Oct 2022 • Longkang Li, Siyuan Liang, Zihao Zhu, Chris Ding, Hongyuan Zha, Baoyuan Wu
Compared to the state-of-the-art reinforcement learning method, our model's network parameters are reduced to only 37\% of theirs, and the solution gap of our model towards the expert solutions decreases from 6. 8\% to 1. 3\% on average.
no code implementations • 16 Sep 2022 • Siyuan Liang, Longkang Li, Yanbo Fan, Xiaojun Jia, Jingzhi Li, Baoyuan Wu, Xiaochun Cao
Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model information.
1 code implementation • 5 Jul 2022 • Longkang Li, Baoyuan Wu
Integer programming (IP) is an important and challenging problem.
no code implementations • 10 Aug 2020 • Longkang Li, Hui-Ling Zhen, Mingxuan Yuan, Jiawen Lu, XialiangTong, Jia Zeng, Jun Wang, Dirk Schnieders
In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism.