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pro vyhledávání: '"Pesare, Andrea"'
We focus on the control of unknown Partial Differential Equations (PDEs). The system dynamics is unknown, but we assume we are able to observe its evolution for a given control input, as typical in a Reinforcement Learning framework. We propose an al
Externí odkaz:
http://arxiv.org/abs/2308.04068
We consider an LQR optimal control problem with partially unknown dynamics. We propose a new model-based online algorithm to obtain an approximation of the dynamics $and$ the control at the same time during a single simulation.
Comment: 8 pages,
Comment: 8 pages,
Externí odkaz:
http://arxiv.org/abs/2105.13723
We deal with the convergence of the value function of an approximate control problem with uncertain dynamics to the value function of a nonlinear optimal control problem. The assumptions on the dynamics and the costs are rather general and we assume
Externí odkaz:
http://arxiv.org/abs/2105.13708
Publikováno v:
Math. Control Signals Syst. (2021)
In 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 $\pi$
Externí odkaz:
http://arxiv.org/abs/2011.03447
Akademický článek
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Autor:
Pesare, Andrea1 (AUTHOR), Palladino, Michele2 (AUTHOR), Falcone, Maurizio1 (AUTHOR) falcone@mat.uniroma1.it
Publikováno v:
Mathematics of Control, Signals & Systems. Sep2021, Vol. 33 Issue 3, p379-411. 33p.
