Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems
Tobias Ehring,
Bernard Haasdonk
Abstract:Numerical methods for the optimal feedback control of high-dimensional dynamical systems typically suffer from the curse of dimensionality. In the current presentation, we devise a mesh-free data-based approximation method for the value function of optimal control problems, which partially mitigates the dimensionality problem. The method is based on a greedy Hermite kernel interpolation scheme and incorporates context knowledge by its structure. Especially, the value function surrogate is elegantly enforced to… Show more
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