Stochastic Optimal Control via Measure Relaxations

Etienne Buehrle, Christoph Stiller
7/26/2025
cs.LGmath.OC

Official code: ebuehrle/dpoc (1 star, last commit 2026-03-30).

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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Cite this paper

@article{buehrle2025stochastic,
  title  = {Stochastic Optimal Control via Measure Relaxations},
  author = {Etienne Buehrle and Christoph Stiller},
  year   = {2025},
  eprint = {2508.00886},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.00886v2}
}

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