Publications

Book Chapters

  • 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.

  • Selected Journal Papers

  • 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), under review.

  • 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), under review.

  • 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.

  • Dynamic power distribution system management with a locally connected communication network

    • K. Zhang, W. Shi, H. Zhu, E. Dall’Anese, and T. Başar

    • IEEE Journal of Selected Topics in Signal Processing (JSTSP), vol. 12, no. 4, pp. 673-687, May, 2018.

  • Selected Conference Papers

  • Near-optimal model-free reinforcement learning in non-stationary episodic MDPs

    • W. Mao, K. Zhang, R. Zhu, D. Simchi-Levi, and T. Başar

    • Intl. Conf. on Machine Learning (ICML), virtual, 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), virtual, 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), virtual, 2020. (Long version under review at 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), virtual, 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) , Berkeley, CA, 2020. (Long version under review at SICON)

  • Policy optimization provably converges to Nash equilibria in zero-sum linear quadratic games

    • K. Zhang, Z. Yang, and T. Başar

    • Neural Info. Process. Systems (NeurIPS), Vancouver, Canada, 2019.

  • 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), Stockholm, Sweden, 2018.

  • On the performance of map-aware cooperative localization

    • K. Zhang, Y. Shen, and M. Z. Win

    • IEEE Intl. Conf. on Commun. (ICC), Kuala Lumpur, Malaysia, 2016.