# MPC-Inspired Neural Network Policies for Sequential Decision Making

arXiv: https://arxiv.org/abs/1802.05803 · HTML: https://mlsanity.com/paper/1802.05803/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 13 of 13)

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- [Neural Lyapunov Differentiable Predictive Control](https://mlsanity.com/paper/2205.10728/) (2022)
- [Deep Model Predictive Optimization](https://mlsanity.com/paper/2310.04590/) (2023)
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- [Online Control-Informed Learning](https://mlsanity.com/paper/2410.03924/) (2024)
- [Cloth Manipulation Planning on Basis of Mesh Representations with Incomplete Domain Knowledge and Voxel-to-Mesh Estimation](https://mlsanity.com/paper/2103.08137/) (2021)
- [Safe Online Control-Informed Learning](https://mlsanity.com/paper/2512.13868/) (2025)

## Abstract (arXiv)

In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making. We introduce an extension to the DAgger algorithm for training such policies and show how they have improved training performance and generalization capabilities. We take advantage of this extension to show scalable and efficient training of complex planning policy architectures in continuous state and action spaces. We provide an extensive comparison of neural network policies by considering feed forward policies, recurrent policies, and recurrent policies with planning structure inspired by the Path Integral control framework. Our results suggest that MPC-type recurrent policies have better robustness to disturbances and modeling error.