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Publications

See timely updated list at Google Scholar.

Monographs

  • 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, 2023 (Invited & Refereed).

  • Independent Learning in Stochastic Games

    • A. Ozdaglar*, M. O. Sayin*, K. Zhang*

    • International Congress of Mathematicians (ICM), 2022 (Invited).

  • Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms

    • K. Zhang, Z. Yang, and T. Başar

    • Springer Studies in Systems, Decision and Control, Handbook on RL and Control, 2020 (Invited).

    • Journal Papers

  • Partially observable multi-agent reinforcement learning with information sharing

    • X. Liu and K. Zhang

    • SIAM Journal on Control and Optimization (SICON) (under review) (Short version appeared at ICML 2023).

  • Policy optimization for H-2 linear control with H-infinity robustness guarantee: Implicit regularization and global convergence

    • K. Zhang, B. Hu, and T. Başar

    • SIAM Journal on Control and Optimization (SICON), 2021.

  • 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, and T. Başar

    • Management Science (MS), 2023.

  • Model-based multi-agent RL in zero-sum Markov games with near-optimal sample complexity

    • K. Zhang, S.M. Kakade, T. Başar, and L.F. Yang

    • Journal of Machine Learning Research (JMLR) 2023 (Short version appeared at NeurIPS 2020 (Spotlight)).

  • Finite-sample analysis for decentralized batch multi-agent reinforcement learning with networked agents

    • K. Zhang, Z. Yang, H. Liu, T. Zhang, and T. Başar

    • IEEE Trans. on Automatic Control (TAC), 2021.

  • Global convergence of policy gradient methods to (almost) locally optimal policies

    • K. Zhang, A. Koppel, H. Zhu, and T. Başar

    • SIAM Journal on Control and Optimization (SICON), 2020.

  • Distributed learning of average belief over networks using sequential observations

    • K. Zhang, Y. Liu, J. Liu, M. Liu, and T. Başar

    • Automatica, 2020.

  • Projected stochastic primal-dual method for constrained online learning with kernels

    • A. Koppel*, K. Zhang*, H. Zhu, and T. Başar

    • IEEE Trans. on Signal Processing (TSP), 2019.

    • Selected Conference Papers

  • Provable partially observable reinforcement learning with privileged information

    • Y. Cai*, X. Liu*, A. Oikonomou*, K. Zhang*

    • Neural Info. Process. Systems (NeurIPS), 2024.

  • Multi-player zero-sum Markov games with networked separable interactions

    • C. Park*, K. Zhang*, and A. Ozdaglar

    • Neural Info. Process. Systems (NeurIPS), 2023.

  • The complexity of Markov equilibrium in stochastic games

    • C. Daskalakis*, N. Golowich*, and K. Zhang*

    • Conference on Learning Theory (COLT), 2023.

  • Tackling combinatorial distribution shift: A matrix completion perspective

    • M. Simchowitz, A. Gupta, and K. Zhang

    • Conference on Learning Theory (COLT), 2023.

  • Breaking the curse of multiagents in a large state space: RL in Markov games with independent linear function approximation

    • Q. Cui, K. Zhang, and S. Du

    • Conference on Learning Theory (COLT), 2023.

  • Partially observable multi-agent RL with (quasi-)efficiency: The blessing of information sharing

    • X. Liu and K. Zhang

    • Intl. Conf. on Machine Learning (ICML), 2023.

  • Revisiting the linear-programming framework for offline RL with general function approximation

    • A. Ozdaglar*, S. Pattathil*, J. Zhang*, and K. Zhang*

    • Intl. Conf. on Machine Learning (ICML), 2023.

  • Can direct latent model learning solve linear quadratic Gaussian control?

    • Y. Tian, K. Zhang, R. Tedrake, and S. Sra

    • Learning for Dynamics & Control (L4DC) (Oral), 2023.

  • What is a good metric to study generalization of minimax learners?

    • A. Ozdaglar*, S. Pattathil*, J. Zhang*, and K. Zhang*

    • Neural Info. Process. Systems (NeurIPS), 2022. (Oral (4 out of all submissions) in New Frontiers in Adversarial Machine Learning Workshop, ICML 2022)

  • Independent policy gradient for large-scale Markov potential games: Sharper rates, function approximation, and game-agnostic convergence

    • D. Ding*, C. Wei*, K. Zhang*, and M. Jovanović

    • Intl. Conf. on Machine Learning (ICML) (Long Oral), 2022.

  • Do differentiable simulators give better policy gradients?

    • H. T. Suh, M. Simchowitz, K. Zhang, and R. Tedrake

    • Intl. Conf. on Machine Learning (ICML) (Outstanding Paper Award), 2022.

  • Decentralized Q-Learning in zero-sum Markov games

    • M. O. Sayin*, K. Zhang*, D. Leslie, T. Başar, and A. Ozdaglar

    • Neural Info. Process. Systems (NeurIPS), 2021.

  • Learning safe multi-agent control with decentralized neural barrier certificates

    • Z. Qin, K. Zhang, Y. Chen, J. Chen, and C. Fan

    • Intl. Conf. on Learning Represent. (ICLR), 2021.

  • Model-based multi-agent RL in zero-sum Markov games with near-optimal sample complexity

    • K. Zhang, S.M. Kakade, T. Başar, and L.F. Yang

    • Neural Info. Process. Systems (NeurIPS) (Spotlight), 2020 (Long version accepted to JMLR).

  • Natural policy gradient primal-dual method for constrained Markov decision processes

    • D. Ding, K. Zhang, T. Başar, and M.R. Jovanovic

    • Neural Info. Process. Systems (NeurIPS), 2020.

  • Policy optimization for H-2 linear control with H-infinity robustness guarantee: Implicit regularization and global convergence

    • K. Zhang, B. Hu, and T. Başar

    • Learning for Dynamics & Control (L4DC) (Oral), 2020 (Long version accepted to SICON).

  • Fully decentralized multi-agent reinforcement learning with networked agents

    • K. Zhang, Z. Yang, H. Liu, T. Zhang, and T. Başar

    • Intl. Conf. on Machine Learning (ICML), 2018.

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