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, 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. L. Ballotta, M. R. Jovanovic, and L. Schenato. Optimal network topology of multi-agent systems subject to computation and communication latency. In Proceedings of the 29th Mediterranean Conference on Control and Automation, Bari, Italy, pages 249-254, 2021. Keyword(s): Controller architecture, Fundamental limitations, Networks, Networks of dynamical systems, Noise amplification, Performance bounds, Topology design. [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 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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