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arXiv · 2508.00886

Stochastic Optimal Control via Measure Relaxations

Etienne Buehrle·Christoph Stiller
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Abstract

The optimal control problem of stochastic systems is commonly solved via robust or scenario-based optimization methods, which are both challenging to scale to long optimization horizons. We cast the optimal control problem of a stochastic system as a convex optimization problem over occupation measures. We demonstrate our method on a set of synthetic and real-world scenarios, learning cost functions from data via Christoffel polynomials. The code for our experiments is available at https://github.com/ebuehrle/dpoc.

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Etienne Buehrle, Christoph Stiller. 2025-07-26. Stochastic Optimal Control via Measure Relaxations. https://arxiv.org/abs/2508.00886

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