| 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 | 1522 | 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 | 676 | 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 | 220* | 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 | 205* | 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 | 167* | 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 | 145 | 2023 |
| 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 | 140 | 2019 |
| 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 | 125 | 2021 |
| 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 | 115 | 2018 |
| 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 | 115* | 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 | 112 | 2020 |
| 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 | 105* | 2020 |
| 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 | 103 | 2016 |
| 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 | 101* | 2018 |
| 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 | 98 | 2021 |
| 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 | 90 | 2020 |
| Do differentiable simulators give better policy gradients? HJ Suh, M Simchowitz, K Zhang, R Tedrake International Conference on Machine Learning 162, 20668-20696, 2022 | 84 | 2022 |
| 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 | 77* | 2017 |
| 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 | 74 | 2022 |
| The complexity of Markov equilibrium in stochastic games C Daskalakis, N Golowich, K Zhang The Thirty Sixth Annual Conference on Learning Theory, 4180-4234, 2023 | 70 | 2023 |
Tamer BaşarSwanlund Endowed Chair Emeritus & CAS Professor Emeritus of ECE, University of IllinoisVerified email at illinois.edu
