1. H. Mohammadi, A. Zare, M. Soltanolkotabi, and M. R. Jovanovic. Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem. IEEE Trans. Automat. Control, 2021. Note: Doi:10.1109/TAC.2021.3087455; also arXiv:1912.11899. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]

1. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. On the lack of gradient domination for linear quadratic Gaussian problems with incomplete state information. In Proceedings of the 60th IEEE Conference on Decision and Control, Austin, TX, 2021. Note: Submitted. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]



2. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. Learning the model-free linear quadratic regulator via random search. In Proceedings of Machine Learning Research, 2nd Annual Conference on Learning for Dynamics and Control, volume 120, Berkeley, CA, pages 1-9, 2020. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]



3. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. Random search for learning the linear quadratic regulator. In Proceedings of the 2020 American Control Conference, Denver, CO, pages 4798-4803, 2020. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]



4. H. Mohammadi, A. Zare, M. Soltanolkotabi, and M. R. Jovanovic. Global exponential convergence of gradient methods over the nonconvex landscape of the linear quadratic regulator. In Proceedings of the 58th IEEE Conference on Decision and Control, Nice, France, pages 7474-7479, 2019. Keyword(s): Data-driven control, Global exponential stability, Gradient descent, Gradient-flow dynamics, Model-free control, Nonconvex optimization, Optimization, Optimal control, Reinforcement learning. [bibtex-entry]



5. S. Hassan-Moghaddam and M. R. Jovanovic. On the exponential convergence rate of proximal gradient flow algorithms. In Proceedings of the 57th IEEE Conference on Decision and Control, Miami, FL, pages 4246-4251, 2018. Note: (Invited paper). Keyword(s): Control for optimization, Distributed optimization, Forward-backward envelope, Exponential convergence, Global exponential stability, Gradient flow dynamics, Large-scale systems, Non-smooth optimization, Primal-dual method, Proximal algorithms, Proximal augmented Lagrangian. [bibtex-entry]



6. H. Mohammadi, M. Razaviyayn, and M. R. Jovanovic. On the stability of gradient flow dynamics for a rank-one matrix approximation problem. In Proceedings of the 2018 American Control Conference, Milwaukee, WI, pages 4533-4538, 2018. Keyword(s): Nonconvex optimization, Stability of nonlinear systems, Matrix approximation, Gradient flow dynamics. [bibtex-entry]

1. H. Mohammadi, M. Soltanolkotabi, and M. R. Jovanovic. Model-free linear quadratic regulator. In K. G. Vamvoudakis, Y. Wan, F. Lewis, and D. Cansever, editors, Handbook of Reinforcement Learning and Control. Springer International Publishing, 2021. Note: Doi:10.1007/978-3-030-60990-0. Keyword(s): Data-driven control, Gradient descent, Gradient-flow dynamics, Linear quadratic regulator, Model-free control, Nonconvex optimization, Optimization, Optimal control, Polyak-Lojasiewicz inequality, Random search method, Reinforcement learning, Sample complexity. [bibtex-entry]

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All person copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

This document was translated from BibT_EX by bibtex2html