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Publications of D. Ding
Theses
  1. D. Ding. Provable reinforcement learning for constrained and multi-agent control systems. PhD thesis, University of Southern California, 2022. Keyword(s): Constrained Markov decision processes, Constrained nonconvex optimization, Function approximation, Game-agnostic convergence, Multi-agent reinforcement learning, Multi-agent systems, Natural policy gradient, Policy gradient methods, Proximal policy optimization, Primal-dual algorithms, Reinforcement learning, Safe exploration, Safe reinforcement learning, Sample complexity, Stochastic optimization. [bibtex-entry]


Journal articles
  1. D. Ding, K. Zhang, J. Duan, T. Basar, and M. R. Jovanovic. Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs. J. Mach. Learn. Res., 2022. Note: Submitted; also arXiv:2206.02346. Keyword(s): Constrained Markov decision processes, Constrained nonconvex optimization, Function approximation, Natural policy gradient, Policy gradient methods, Primal-dual algorithms, Sample complexity. [bibtex-entry]


  2. D. Ding, X. Wei, Z. Yang, Z. Wang, and M. R. Jovanovic. Fast multi-agent temporal-difference learning via homotopy stochastic primal-dual optimization. IEEE Trans. Automat. Control, 2020. Note: Submitted; also arXiv:1908.02805. Keyword(s): Convex optimization, Distributed temporal-difference learning, Multi-agent systems, Primal-dual algorithms, Reinforcement learning, Stochastic optimization. [bibtex-entry]


Conference articles
  1. D. Ding and M. R. Jovanovic. Policy gradient primal-dual mirror descent for constrained MDPs with large state spaces. In Proceedings of the 61st IEEE Conference on Decision and Control, Cancun, Mexico, 2022. Note: To appear. Keyword(s): Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms, Mirror descent, Function approximation. [bibtex-entry]


  2. D. Ding, C.-Y. Wei, K. Zhang, and M. R. Jovanovic. Independent policy gradient for large-scale Markov potential games: sharper rates, function approximation, and game-agnostic convergence. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, Baltimore, MD, pages 5166-5220, 2022. Keyword(s): Multi-agent reinforcement learning, Independent reinforcement learning, Policy gradient methods, Markov potential games, Function approximation, Game-agnostic convergence. [bibtex-entry]


  3. D. Ding, K. Zhang, T. Basar, and M. R. Jovanovic. Convergence and optimality of policy gradient primal-dual method for constrained Markov decision processes. In Proceedings of the 2022 American Control Conference, Atlanta, GA, pages 2851-2856, 2022. Keyword(s): Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms. [bibtex-entry]


  4. D. Ding, X. Wei, Z. Yang, Z. Wang, and M. R. Jovanovic. Provably efficient safe exploration via primal-dual policy optimization. In 24th International Conference on Artificial Intelligence and Statistics, volume 130, Virtual, pages 3304-3312, 2021. Keyword(s): Safe reinforcement learning, Constrained Markov decision processes, Safe exploration, Proximal policy optimization, Non-convex optimization, Online mirror descent, Primal-dual method. [bibtex-entry]


  5. D. Ding, X. Wei, H. Yu, and M. R. Jovanovic. Byzantine-resilient distributed learning under constraints. In Proceedings of the 2021 American Control Conference, New Orleans, LA, pages 2260-2265, 2021. Keyword(s): Byzantine primal-dual optimization, Constrained optimization, Distributed optimization, Robust statistical learning. [bibtex-entry]


  6. D. Ding, J. Yuan, and M. R. Jovanovic. Discounted online Newton method for time-varying time series prediction. In Proceedings of the 2021 American Control Conference, New Orleans, LA, pages 1547-1552, 2021. Keyword(s): Discounted online Newton method, Time-varying time series prediction, Online learning, COVID-19 prediction. [bibtex-entry]


  7. D. Ding and M. R. Jovanovic. Global exponential stability of primal-dual gradient flow dynamics based on the proximal augmented Lagrangian: A Lyapunov-based approach. In Proceedings of the 59th IEEE Conference on Decision and Control, Jeju Island, Republic of Korea, pages 4836-4841, 2020. Keyword(s): Augmented Lagrangian, Control for optimization, Convex optimization, Global exponential stability, Lyapunov-based approach, Non-smooth optimization, Primal-dual gradient flow dynamics, Primal-dual methods, Proximal augmented Lagrangian. [bibtex-entry]


  8. D. Ding, K. Zhang, T. Basar, and M. R. Jovanovic. Natural policy gradient primal-dual method for constrained Markov decision processes. In Proceedings of the 34th Conference on Neural Information Processing Systems, volume 33, Vancouver, Canada, pages 8378-8390, 2020. Keyword(s): Constrained Markov decision processes, Constrained nonconvex optimization, Natural policy gradient, Policy gradient methods, Primal-dual algorithms. [bibtex-entry]


  9. D. Ding and M. R. Jovanovic. Global exponential stability of primal-dual gradient flow dynamics based on the proximal augmented Lagrangian. In Proceedings of the 2019 American Control Conference, Philadelphia, PA, pages 3414-3419, 2019. Keyword(s): Convex optimization, Global exponential stability, Non-smooth optimization, Primal-dual gradient flow dynamics, Proximal augmented Lagrangian method. [bibtex-entry]


  10. D. Ding, X. Wei, and M. R. Jovanovic. Distributed robust statistical learning: Byzantine mirror descent. In Proceedings of the 58th IEEE Conference on Decision and Control, Nice, France, pages 1822-1827, 2019. Keyword(s): Byzantine mirror descent, Distributed optimization, Dual averaging, Robust statistical learning. [bibtex-entry]


  11. D. Ding, X. Wei, Z. Yang, Z. Wang, and M. R. Jovanovic. Fast multi-agent temporal-difference learning via homotopy stochastic primal-dual method. In Optimization Foundations for Reinforcement Learning Workshop, 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, 2019. Keyword(s): Convex optimization, Distributed temporal-difference learning, Multi-agent systems, Primal-dual algorithms, Reinforcement learning, Stochastic optimization. [bibtex-entry]


  12. D. Ding, B. Hu, N. K. Dhingra, and M. R. Jovanovic. An exponentially convergent primal-dual algorithm for nonsmooth composite minimization. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 4927-4932, 2018. Keyword(s): Control for optimization, Convex optimization, Euler discretization, Exponential convergence, Global exponential stability, Integral quadratic constraints, Proximal augmented Lagrangian, Non-smooth optimization, Primal-dual gradient flow dynamics, Proximal algorithms, Regularization. [bibtex-entry]


  13. D. Ding and M. R. Jovanovic. A primal-dual Laplacian gradient flow dynamics for distributed resource allocation problems. In Proceedings of the 2018 American Control Conference, Milwaukee, WI, pages 5316-5320, 2018. Keyword(s): Primal-dual gradient flow dynamics, Proximal augmented Lagrangian, Distributed resource allocation, Economic dispatch. [bibtex-entry]



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