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Kaiqing Zhang
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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
17792021
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
7442018
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
247*2019
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
228*2022
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
176*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
1582023
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
1572021
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
1502019
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
133*2018
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
1222020
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
1222018
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
115*2020
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
1132021
Do differentiable simulators give better policy gradients?
HJ Suh, M Simchowitz, K Zhang, R Tedrake
International Conference on Machine Learning 162, 20668-20696, 2022
1112022
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
1112016
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
1052020
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
103*2018
Towards a theoretical foundation of policy optimization for learning control policies
B Hu, K Zhang, N Li, M Mesbahi, M Fazel, T Başar
Annual Review of Control, Robotics, and Autonomous Systems, 123-158, 2023
872023
Independent policy gradient for large-scale Markov potential games: Sharper rates, function approximation, and game-agnostic convergence
D Ding, CY Wei, K Zhang, MR Jovanović
International Conference on Machine Learning 162, 5166-5220, 2022
862022
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
83*2017
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