Kaiqing Zhang

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Tamer BaşarSwanlund Endowed Chair Emeritus & CAS Professor Emeritus of ECE, University of IllinoisVerified email at illinois.edu
Zhuoran YangYale UniversityVerified email at yale.edu
Hao ZhuAssociate Professor of ECE, The University of Texas at AustinVerified email at utexas.edu
Tong ZhangHKUSTVerified email at tongzhang-ml.org
Asuman OzdaglarEECS, MITVerified email at mit.edu
Russ TedrakeMIT (EECS, MechE, Aero/Astro) and Toyota Research InstituteVerified email at mit.edu
Mihailo R. JovanovicProfessor of Electrical and Computer Engineering, University of Southern CaliforniaVerified email at usc.edu
MAX SIMCHOWITZMITVerified email at mit.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Chen-Yu WeiAssistant Professor, University of VirginiaVerified email at virginia.edu
Wotao YinDAMO Academy, Alibaba Group USVerified email at alibaba-inc.com
H.J. Terry SuhMassachusetts Institute of TechnologyVerified email at mit.edu
Chuchu FanWilson Assistant Professor of Aeronautics and Astronautics at MITVerified email at mit.edu
Yuan ShenProfessor, EE, Tsinghua UniversityVerified email at tsinghua.edu.cn
Noah GolowichMITVerified email at mit.edu
Constantinos DaskalakisProfessor of Computer Science, MITVerified email at csail.mit.edu
Maryam FazelProfessor of Electrical and Computer Engineering, University of WashingtonVerified email at uw.edu
Mehran MesbahiUniversity of WashingtonVerified email at aa.washington.edu
Na LiWinokur Family Professor of Electrical Engineering and Applied Mathematics, HarvardVerified email at seas.harvard.edu
Mingyan LiuProfessor of Electrical Engineering and Computer Science, University of MichiganVerified email at umich.edu

Kaiqing Zhang

Assistant Professor, University of Maryland, College Park

Verified email at umd.edu - Homepage

Control Machine Learning Game Theory Reinforcement Learning Robotics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Multi-agent reinforcement learning: A selective overview of theories and algorithms K Zhang, Z Yang, T Başar Studies in Systems, Decision and Control, Handbook on RL and Control, 2021	999	2021
Fully decentralized multi-agent reinforcement learning with networked agents K Zhang, Z Yang, H Liu, T Zhang, T Başar International Conference on Machine Learning (ICML), 2018	552	2018
Global convergence of policy gradient methods to (almost) locally optimal policies K Zhang, A Koppel, H Zhu, T Başar SIAM Journal on Control and Optimization (SICON), 2019	168*	2019
Policy optimization for linear control with robustness guarantee: Implicit regularization and global convergence K Zhang, B Hu, T Başar SIAM Journal on Control and Optimization (SICON), 2021	116*	2021
Model-based multi-agent RL in zero-sum Markov games with near-optimal sample complexity K Zhang, SM Kakade, T Basar, LF Yang Journal of Machine Learning Research 24 (175), 1-53, 2023	114	2023
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs D Ding, K Zhang, J Duan, T Başar, MR Jovanović arXiv preprint arXiv:2206.02346, 2022	114*	2022
Policy optimization provably converges to Nash equilibria in zero-sum linear quadratic games K Zhang, Z Yang, T Basar Advances in Neural Information Processing Systems, 11602-11614, 2019	112	2019
Networked multi-agent reinforcement learning in continuous spaces K Zhang, Z Yang, T Basar 2018 IEEE Conference on Decision and Control (CDC), 2771-2776, 2018	96	2018
Dependency analysis and improved parameter estimation for dynamic composite load modeling K Zhang, H Zhu, S Guo IEEE Transactions on Power Systems 32 (4), 3287-3297, 2016	93	2016
Communication-efficient policy gradient methods for distributed reinforcement learning T Chen, K Zhang, GB Giannakis, T Başar IEEE Transactions on Control of Network Systems, 2018	87*	2018
A multi-agent off-policy actor-critic algorithm for distributed reinforcement learning W Suttle, Z Yang, K Zhang, Z Wang, T Başar, J Liu IFAC-PapersOnLine 53 (2), 1549-1554, 2020	83*	2020
An improved analysis of (variance-reduced) policy gradient and natural policy gradient methods Y Liu, K Zhang, T Basar, W Yin Advances in Neural Information Processing Systems 33, 2020	74	2020
Consumption behavior analytics-aided energy forecasting and dispatch Y Zhang, R Yang, K Zhang, H Jiang, JJ Zhang IEEE Intelligent Systems 32 (4), 59-63, 2017	69*	2017
Finite-sample analysis for decentralized batch multi-agent reinforcement learning with networked agents K Zhang, Z Yang, H Liu, T Zhang, T Başar IEEE Transactions on Automatic Control, 2018	68*	2018
Learning safe multi-agent control with decentralized neural barrier certificates Z Qin, K Zhang, Y Chen, J Chen, C Fan International Conference on Learning Representations (ICLR), 2021	66	2021
Decentralized Q-Learning in zero-sum Markov games MO Sayin, K Zhang, DS Leslie, T Basar, A Ozdaglar Advances in Neural Information Processing Systems 34, 2021	64	2021
Dynamic power distribution system management with a locally connected communication network K Zhang, W Shi, H Zhu, E Dall'Anese, T Basar IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2018	59*	2018
Robust multi-agent reinforcement learning with model uncertainty K Zhang, T Sun, Y Tao, S Genc, S Mallya, T Basar Advances in Neural Information Processing Systems 33, 2020	54	2020
Machine learning techniques for spectrum sensing when primary user has multiple transmit powers K Zhang, J Li, F Gao 2014 IEEE International Conference on Communication Systems, 137-141, 2014	47*	2014
Model-free non-stationary RL: Near-optimal regret and applications in multi-agent RL and inventory control W Mao, K Zhang, R Zhu, D Simchi-Levi, T Başar arXiv preprint arXiv:2010.03161, 2020	45*	2020

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