Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning
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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.
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References · 9
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
- 2016OpenAI Gymcited by 1,736
- 2020Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learningcited by 174
- 2019BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameterscited by 25
- 2019Performance-oriented model learning for data-driven MPC designcited by 22
- 2017Goal-Driven Dynamics Learning via Bayesian Optimizationcited by 19
- 2017Benchmark Environments for Multitask Learning in Continuous Domainscited by 14
- 2020Meta Learning MPC using Finite-Dimensional Gaussian Process Approximationscited by 8
- 2022Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Modelscited by 1
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