Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929297997955072 |
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| author | Li, Baoyu Edwards, William Hauser, Kris |
| author_facet | Li, Baoyu Edwards, William Hauser, Kris |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00232 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning Li, Baoyu Edwards, William Hauser, Kris Robotics Machine Learning 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. |
| title | Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2404.00232 |