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. Provably efficient generalized Lagrangian policy optimization for safe multi-agent reinforcement learning. In Proceedings of 5th Annual Conference on Learning for Dynamics and Control, volume 211 of Proceedings of Machine Learning Research, Philadelphia, PA, pages 315-332, 2023. Keyword(s): Constrained Markov games, Method of Lagrange multipliers, Minimax optimization, Multi-agent reinforcement learning, Primal-dual policy optimization. [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]

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