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Publications about 'Reinforcement learning'


H. Mohammadi,
A. Zare,
M. Soltanolkotabi,
and M. R. Jovanovic.
Convergence and sample complexity of gradient methods for the modelfree linearquadratic regulator problem.
IEEE Trans. Automat. Control,
67(5):24352450,
May 2022.
Keyword(s): Datadriven control,
Gradient descent,
Gradientflow dynamics,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
PolyakLojasiewicz inequality,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]

I. K. Ozaslan,
H. Mohammadi,
and M. R. Jovanovic.
Computing stabilizing feedback gains via a modelfree policy gradient.
IEEE Control Syst. Lett.,
2022.
Note: Submitted.
Keyword(s): Datadriven control,
Gradient descent,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]

H. Mohammadi,
M. Soltanolkotabi,
and M. R. Jovanovic.
On the linear convergence of random search for discretetime LQR.
IEEE Control Syst. Lett.,
5(3):989994,
July 2021.
Keyword(s): Datadriven control,
Gradient descent,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Fast multiagent temporaldifference learning via homotopy stochastic primaldual optimization.
IEEE Trans. Automat. Control,
2020.
Note: Submitted; also arXiv:1908.02805.
Keyword(s): Convex optimization,
Distributed temporaldifference learning,
Multiagent systems,
Primaldual algorithms,
Reinforcement learning,
Stochastic optimization.
[bibtexentry]

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Provably efficient generalized Lagrangian policy optimization for safe multiagent reinforcement learning.
In 39th International Conference on Machine Learning,
Baltimore, MD,
2022.
Note: Submitted.
Keyword(s): Multiagent reinforcement learning,
Constrained Markov games,
Primaldual policy optimization,
Method of Lagrange multipliers,
Minimax optimization.
[bibtexentry]

D. Ding,
C.Y. Wei,
K. Zhang,
and M. R. Jovanovic.
Independent policy gradient for largescale Markov potential games: sharper rates, function approximation, and gameagnostic convergence.
In 39th International Conference on Machine Learning,
Baltimore, MD,
2022.
Note: Submitted.
Keyword(s): Multiagent reinforcement learning,
Independent reinforcement learning,
Policy gradient methods,
Markov potential games,
Function approximation,
Gameagnostic convergence.
[bibtexentry]

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Provably efficient safe exploration via primaldual policy optimization.
In 24th International Conference on Artificial Intelligence and Statistics,
volume 130,
Virtual,
pages 33043312,
2021.
Keyword(s): Safe reinforcement learning,
Constrained Markov decision processes,
Safe exploration,
Proximal policy optimization,
Nonconvex optimization,
Online mirror descent,
Primaldual method.
[bibtexentry]

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,
pages 11201124,
2021.
Keyword(s): Datadriven control,
Gradient descent,
Gradientflow dynamics,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
PolyakLojasiewicz inequality,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]

H. Mohammadi,
M. Soltanolkotabi,
and M. R. Jovanovic.
Learning the modelfree 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 19,
2020.
Keyword(s): Datadriven control,
Gradient descent,
Gradientflow dynamics,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
PolyakLojasiewicz inequality,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]

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 47984803,
2020.
Keyword(s): Datadriven control,
Gradient descent,
Gradientflow dynamics,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
PolyakLojasiewicz inequality,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]

D. Ding,
X. Wei,
Z. Yang,
Z. Wang,
and M. R. Jovanovic.
Fast multiagent temporaldifference learning via homotopy stochastic primaldual method.
In Optimization Foundations for Reinforcement Learning Workshop, 33rd Conference on Neural Information Processing Systems,
Vancouver, Canada,
2019.
Keyword(s): Convex optimization,
Distributed temporaldifference learning,
Multiagent systems,
Primaldual algorithms,
Reinforcement learning,
Stochastic optimization.
[bibtexentry]

S. HassanMoghaddam,
M. R. Jovanovic,
and S. Meyn.
Datadriven proximal algorithms for the design of structured optimal feedback gains.
In Proceedings of the 2019 American Control Conference,
Philadelphia, PA,
pages 58465850,
2019.
Keyword(s): Datadriven feedback design,
Largescale systems,
Nonsmooth optimization,
Proximal algorithms,
Reinforcement learning,
Sparsitypromoting optimal control,
Structured optimal control.
[bibtexentry]

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 74747479,
2019.
Keyword(s): Datadriven control,
Global exponential stability,
Gradient descent,
Gradientflow dynamics,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
Reinforcement learning.
[bibtexentry]

H. Mohammadi,
M. Soltanolkotabi,
and M. R. Jovanovic.
Modelfree 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:10.1007/9783030609900.
Keyword(s): Datadriven control,
Gradient descent,
Gradientflow dynamics,
Linear quadratic regulator,
Modelfree control,
Nonconvex optimization,
Optimization,
Optimal control,
PolyakLojasiewicz inequality,
Random search method,
Reinforcement learning,
Sample complexity.
[bibtexentry]
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Last modified: Tue May 3 09:45:45 2022
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