# Computing Optimal Joint Chance Constrained Control Policies

arXiv: https://arxiv.org/abs/2312.10495 · HTML: https://mlsanity.com/paper/2312.10495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [A learning-based approach to stochastic optimal control under reach-avoid constraint](https://mlsanity.com/paper/2412.16561/) (2024)
- [Joint Chance Constrained Safe-Optimal Control](https://mlsanity.com/paper/2606.30829/) (2026)
- [Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints](https://mlsanity.com/paper/2608.01151/) (2026)

## Abstract (arXiv)

We consider the problem of optimally controlling stochastic, Markovian systems subject to joint chance constraints over a finite-time horizon. For such problems, standard Dynamic Programming is inapplicable due to the time correlation of the joint chance constraints, which calls for non-Markovian, and possibly stochastic, policies. Hence, despite the popularity of this problem, solution approaches capable of providing provably-optimal and easy-to-compute policies are still missing. We fill this gap by augmenting the dynamics via a binary state, allowing us to characterize the optimal policies and develop a Dynamic Programming based solution method.