Policy Optimization for Unknown Systems using Differentiable Model Predictive Control

Riccardo Zuliani; Efe C. Balta; John Lygeros

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arXiv · 2511.11308

Policy Optimization for Unknown Systems using Differentiable Model Predictive Control

Riccardo Zuliani·Efe C. Balta·John Lygeros

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Abstract

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.

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BibTeX RIS

Riccardo Zuliani, Efe C. Balta, John Lygeros. 2025-11-14. Policy Optimization for Unknown Systems using Differentiable Model Predictive Control. https://arxiv.org/abs/2511.11308

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