Efficient Automatic Tuning for Data-driven Model Predictive Control via
  Meta-Learning
Baoyu Li William Edwards Kris Hauser
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.