# Model predictive control with stage cost shaping inspired by reinforcement learning

arXiv: https://arxiv.org/abs/1906.02580 · HTML: https://mlsanity.com/paper/1906.02580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Stochastic Reinforcement Learning with Stability Guarantees for Control of Unknown Nonlinear Systems](https://mlsanity.com/paper/2409.08382/) (2024)
- [A framework for online, stabilizing reinforcement learning](https://mlsanity.com/paper/2207.08730/) (2022)

## Abstract (arXiv)

This work presents a suboptimality study of a particular model predictive control with a stage cost shaping based on the ideas of reinforcement learning. The focus of the suboptimality study is to derive quantities relating the infinite-horizon cost function under the said variant of model predictive control to the respective infinite-horizon value function. The basis control scheme involves usual stabilizing constraints comprising of a terminal set and a terminal cost in the form of a local Lyapunov function. The stage cost is adapted using the principles of Q-learning, a particular approach to reinforcement learning. The work is concluded by case studies with two systems for wide ranges of initial conditions.