Learning optimal feedback operators and their polynomial approximation
A learning based method for obtaining feedback laws for nonlinear optimal control problems
is proposed. The learning problem is posed such that the open loop value function is its
optimal solution. This infinite dimensional, function space, problem, is approximated by a
polynomial ansatz and its convergence is analyzed. An $\ell_1 $ penalty term is employed,
which combined with the proximal point method, allows to find sparse solutions for the
learning problem. The approach requires multiple evaluations of the elements of the …
is proposed. The learning problem is posed such that the open loop value function is its
optimal solution. This infinite dimensional, function space, problem, is approximated by a
polynomial ansatz and its convergence is analyzed. An $\ell_1 $ penalty term is employed,
which combined with the proximal point method, allows to find sparse solutions for the
learning problem. The approach requires multiple evaluations of the elements of the …
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