Generalized Finite-Horizon Linear-Quadratic Optimal Control

2013 ◽  
pp. 1-8
Author(s):  
Augusto Ferrante
Keyword(s):  
Optimal Control ◽  
Finite Horizon ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Quadratic Optimal Control

The linear quadratic optimal control problem for discrete-time Markov jump linear singular systems

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109506
Author(s):  
Jorge R. Chávez-Fuentes ◽  
Eduardo F. Costa ◽  
Marco H. Terra ◽  
Kaio D.T. Rocha
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Discrete Time ◽  
Singular Systems ◽  
Linear Quadratic ◽  
Markov Jump ◽  
Linear Quadratic Optimal Control ◽  
Quadratic Optimal Control

scholarly journals Convergence results for an averaged LQR problem with applications to reinforcement learning

2021 ◽  
Author(s):  
Andrea Pesare ◽  
Michele Palladino ◽  
Maurizio Falcone
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Current System ◽  
Numerical Test ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Lqr Problem ◽  
Quadratic Optimal Control

AbstractIn this paper, we will deal with a linear quadratic optimal control problem with unknown dynamics. As a modeling assumption, we will suppose that the knowledge that an agent has on the current system is represented by a probability distribution $$\pi $$ π on the space of matrices. Furthermore, we will assume that such a probability measure is opportunely updated to take into account the increased experience that the agent obtains while exploring the environment, approximating with increasing accuracy the underlying dynamics. Under these assumptions, we will show that the optimal control obtained by solving the “average” linear quadratic optimal control problem with respect to a certain $$\pi $$ π converges to the optimal control driven related to the linear quadratic optimal control problem governed by the actual, underlying dynamics. This approach is closely related to model-based reinforcement learning algorithms where prior and posterior probability distributions describing the knowledge on the uncertain system are recursively updated. In the last section, we will show a numerical test that confirms the theoretical results.

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