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]

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]

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, pages 4892-4897, 2022. Keyword(s): Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms, Mirror descent, Function approximation. [bibtex-entry]



2. 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]



3. 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]



4. 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]

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