Stochastic Optimal Control via Measure Relaxations

• URL: http://arxiv.org/abs/2508.00886v1
• Date: Sat, 26 Jul 2025 07:18:16 GMT
• Title: Stochastic Optimal Control via Measure Relaxations
• Authors: Etienne Buehrle, Christoph Stiller,
• Abstract summary: We cast the optimal control problem of a system as a convex problem over occupation measures.<n>We demonstrate our method on a set of synthetic and realworld scenarios.
• Score: 5.716199402595783
• License: http://creativecommons.org/licenses/by/4.0/
• 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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