Model-based policy optimization often struggles with inaccurate system dynamics models, leading to suboptimal closed-loop performance. This challenge is especially evident in Model Predictive Control (MPC) policies, which rely on the model for real-time trajectory planning and optimization. We introduce a novel policy optimization framework for MPC-based policies combining differentiable optimization with zeroth-order optimization. Our method combines model-based and model-free gradient estimation approaches, achieving faster transient performance compared to fully data-driven approaches while maintaining convergence guarantees, even under model uncertainty. We demonstrate the effectiveness of the proposed approach on a nonlinear control task involving a 12-dimensional quadcopter model.
@article{arxiv.2511.11308,
title = {Policy Optimization for Unknown Systems using Differentiable Model Predictive Control},
author = {Riccardo Zuliani and Efe C. Balta and John Lygeros},
journal= {arXiv preprint arXiv:2511.11308},
year = {2026}
}