no code implementations • 26 Feb 2024 • Liangqi Liu, Wenqiang Pu, Yingru Li, Bo Jiu, Zhi-Quan Luo
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics.
no code implementations • 7 Feb 2024 • Yingru Li, Liangqi Liu, Wenqiang Pu, Hao Liang, Zhi-Quan Luo
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making.
no code implementations • 21 Feb 2022 • Huayue Li, Zhaowei Han, Wenqiang Pu, Liangqi Liu, Kang Li, Bo Jiu
Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.
no code implementations • 28 Dec 2021 • Bingqing Song, Haoran Sun, Wenqiang Pu, Sijia Liu, Mingyi Hong
We then provide a series of theoretical results to further understand the properties of the two approaches.
no code implementations • 4 Sep 2021 • Wenqiang Pu, Ya-Feng Liu, Zhi-Quan Luo
There are generally two difficulties in this bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, and the other is the highly nonlinear coordinate transformation between the local and global coordinate systems of the sensors.
1 code implementation • 3 May 2021 • Haoran Sun, Wenqiang Pu, Xiao Fu, Tsung-Hui Chang, Mingyi Hong
However, it is often challenging for these approaches to learn in a dynamic environment.
no code implementations • 29 Apr 2021 • Wenqiang Pu, Shahana Ibrahim, Xiao Fu, Mingyi Hong
This work offers a unified stochastic algorithmic framework for large-scale CPD decomposition under a variety of non-Euclidean loss functions.
4 code implementations • 16 Nov 2020 • Haoran Sun, Wenqiang Pu, Minghe Zhu, Xiao Fu, Tsung-Hui Chang, Mingyi Hong
We propose to build the notion of continual learning (CL) into the modeling process of learning wireless systems, so that the learning model can incrementally adapt to the new episodes, {\it without forgetting} knowledge learned from the previous episodes.