# Stochastic Optimal Control via Measure Relaxations

arXiv: https://arxiv.org/abs/2508.00886 · HTML: https://mlsanity.com/paper/2508.00886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 26 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances](https://mlsanity.com/paper/1709.07523/) (2017)
- [COSMO: A conic operator splitting method for convex conic problems](https://mlsanity.com/paper/1901.10887/) (2019)
- [Fully-Automated Verification of Linear Systems Using Inner- and Outer-Approximations of Reachable Sets](https://mlsanity.com/paper/2209.09321/) (2022)
- [Incremental Proximal Multi-Forecast Model Predictive Control](https://mlsanity.com/paper/2111.14728/) (2021)
- [Scenario-based Stochastic MPC for systems with uncertain dynamics](https://mlsanity.com/paper/2207.12517/) (2022)
- [Performance enhancements for a generic conic interior point algorithm](https://mlsanity.com/paper/2107.04262/) (2021)
- [Symmetry reduction and recovery of trajectories of optimal control problems via measure relaxations](https://mlsanity.com/paper/2307.03787/) (2023)

## Abstract (arXiv)

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.