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Kaiqing Zhang
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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
4802021
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
3652018
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
115*2019
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
792019
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
762016
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 59 (6), 4081-4109, 2021
742021
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
702018
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
60*2018
Model-based multi-agent RL in zero-sum Markov games with near-optimal sample complexity
K Zhang, S Kakade, T Başar, L Yang
arXiv preprint arXiv:2007.07461, 2020
572020
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
54*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
52*2018
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
49*2022
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
49*2020
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
46*2018
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
43*2014
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
352020
Optimal joint bidding and pricing of profit-seeking load serving entity
H Xu, K Zhang, J Zhang
IEEE Transactions on Power Systems 33 (5), 5427-5436, 2018
252018
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
242020
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
222021
On the stability and convergence of robust adversarial reinforcement learning: A case study on linear quadratic systems
K Zhang, B Hu, T Basar
Advances in Neural Information Processing Systems 33, 2020
202020
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Articles 1–20