TR2022-161
Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints
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- "Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC51059.2022.9992943, December 2022.BibTeX TR2022-161 PDF
- @inproceedings{Paulson2022dec,
- author = {Paulson, Joel A. and Sorouifar, Farshud and Chakrabarty, Ankush},
- title = {Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2022,
- month = dec,
- doi = {10.1109/CDC51059.2022.9992943},
- url = {https://www.merl.com/publications/TR2022-161}
- }
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- "Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC51059.2022.9992943, December 2022.
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Research Areas:
Abstract:
Bayesian optimization (BO) is a data-efficient approach for optimizing expensive-to-evaluate black-box functions that suffer from noisy evaluations. Traditional BO algorithms ignore the relationship between consecutive input values, which is known to lead to significant “jumps” in the search space that cannot be implemented in practice, especially in online experimental systems. For example, in performance-driven control applications, large changes in the chosen setpoint parameters may trigger fail-safe mechanisms or lead to violation of critical safety constraints. In such applications, it is necessary to limit the allowable search space at each BO iteration, which can be done by incorporating local search constraints into the original problem setting. In this paper, we show how this novel BO setting can be cast as a Markov decision process (MDP) for which the optimal policy is characterized by an intractable dynamic programming (DP) problem. To overcome this challenge, we take advantage of approximate DP methods, particularly rollout with fast policy search, to derive an efficient multi-step lookahead BO policy. We also propose a novel base policy needed for the rollout algorithm, which explicitly incorporates the local search restrictions in an efficient and intuitive manner. Lastly, we empirically show that our proposed multi-step lookahead BO policy outperforms existing methods on a well-known benchmark problem.
Related News & Events
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NEWS MERL Researchers Presented Six Papers at the 2022 IEEE Conference on Decision and Control (CDC’22) Date: December 6, 2022 - December 9, 2022
Where: Cancún, Mexico
MERL Contacts: Ankush Chakrabarty; Devesh K. Jha; Arvind Raghunathan; Diego Romeres; Yebin Wang
Research Areas: Control, OptimizationBrief- MERL researchers presented six papers at the Conference on Decision and Control that was held in Cancún, Mexico from December 6-9, 2022. The papers covered a broad range of topics in the areas of decision making and control, including Bayesian optimization, quadratic programming, solution of differential equations, distributed Kalman filtering, thermal monitoring of batteries, and closed-loop control optimization.