# Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning

arXiv: https://arxiv.org/abs/2404.00232 · HTML: https://mlsanity.com/paper/2404.00232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 30 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters](https://mlsanity.com/paper/1908.06756/) (2019)
- [Performance-oriented model learning for data-driven MPC design](https://mlsanity.com/paper/1904.10839/) (2019)
- [Goal-Driven Dynamics Learning via Bayesian Optimization](https://mlsanity.com/paper/1703.09260/) (2017)
- [Benchmark Environments for Multitask Learning in Continuous Domains](https://mlsanity.com/paper/1708.04352/) (2017)
- [Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations](https://mlsanity.com/paper/2008.05984/) (2020)
- [Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models](https://mlsanity.com/paper/2209.09358/) (2022)

## Abstract (arXiv)

AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization (BO). To address these issues, this paper proposes to employ a meta-learning approach called Portfolio that improves AutoMPC's efficiency and stability by warmstarting BO. Portfolio optimizes initial designs for BO using a diverse set of configurations from previous tasks and stabilizes the tuning process by fixing initial configurations instead of selecting them randomly. Experimental results demonstrate that Portfolio outperforms the pure BO in finding desirable solutions for AutoMPC within limited computational resources on 11 nonlinear control simulation benchmarks and 1 physical underwater soft robot dataset.