Stochastic Optimal Control via Measure Relaxations
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912589489897472 |
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| author | Buehrle, Etienne Stiller, Christoph |
| author_facet | Buehrle, Etienne Stiller, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00886 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic Optimal Control via Measure Relaxations Buehrle, Etienne Stiller, Christoph Machine Learning Optimization and Control 90C22, 93C10, 28A99 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. |
| title | Stochastic Optimal Control via Measure Relaxations |
| topic | Machine Learning Optimization and Control 90C22, 93C10, 28A99 |
| url | https://arxiv.org/abs/2508.00886 |