1. 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]

1. 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 60th IEEE Conference on Decision and Control, Austin, TX, 2021. Note: Submitted. Keyword(s): Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms, Minimax optimization. [bibtex-entry]



2. 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, Vancouver, Canada, 2020. Keyword(s): Constrained Markov decision processes, Policy gradient methods, Primal-dual algorithms, Minimax optimization. [bibtex-entry]



3. 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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