# Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty

arXiv: https://arxiv.org/abs/2601.01940 · HTML: https://mlsanity.com/paper/2601.01940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem](https://mlsanity.com/paper/2607.16084/) (2026)

## Abstract (arXiv)

Model-based policy optimization is a well-established framework for designing reliable and high-performance controllers across a wide range of control applications. Recently, this approach has been extended to model predictive control policies, where explicit dynamical models are embedded within the control law. However, the performance of the resulting controllers, and the convergence of the associated optimization algorithms, critically depends on the accuracy of the models. In this paper, we demonstrate that combining gradient-based policy optimization with recursive system identification ensures convergence to an optimal controller design and showcase our finding in several control examples.