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New data-driven method tackles nonlinear optimal control problems

By PulseAugur Editorial · [1 sources] ·

This paper introduces a novel data-driven method for optimal control of nonlinear systems, specifically focusing on stochastic diffusions where system dynamics and cost functions are unknown. By embedding state probability densities into a reproducing kernel Hilbert space (RKHS), the approach leverages operator regression to learn Markov transition operators. This method integrates with convex operator-theoretic Hamilton-Jacobi-Bellman recursions, enabling efficient solutions for nonlinear optimal control problems, as demonstrated by its application to regulating an autonomous underwater vehicle. AI

IMPACT This approach could enable more sophisticated control systems in robotics and autonomous vehicles by learning complex dynamics from data.

RANK_REASON The cluster contains a single arXiv preprint detailing a new methodology in a specific research area (optimal control). [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New data-driven method tackles nonlinear optimal control problems

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The cluster contains a single arXiv preprint detailing a new methodology in a specific research area (optimal control). [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Nicolas Hoischen, Petar Bevanda, Stefan Sosnowski, Sandra Hirche, Boris Houska ·

    Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces

    arXiv:2407.16407v3 Announce Type: replace-cross Abstract: This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost …

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