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Parallelized Bayesian Optimization for Expensive Robot Controller Evolution

  • An important class of black-box optimization problems relies on using simulations to assess the quality of a given candidate solution. Solving such problems can be computationally expensive because each simulation is very time-consuming. We present an approach to mitigate this problem by distinguishing two factors of computational cost: the number of trials and the time needed to execute the trials. Our approach tries to keep down the number of trials by using Bayesian optimization (BO) –known to be sample efficient– and reducing wall-clock times by parallel execution of trials. We compare the performance of four parallelization methods and two model-free alternatives. Each method is evaluated on all 24 objective functions of the Black-Box-Optimization- Benchmarking (BBOB) test suite in their five, ten, and 20-dimensional versions. Additionally, their performance is investigated on six test cases in robot learning. The results show that parallelized BO outperforms the state-of-the-art CMA-ES on the BBOB test functions, especially for higher dimensions. On the robot learning tasks, the differences are less clear, but the data do support parallelized BO as the ‘best guess’, winning on some cases and never losing.

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Author:Margarita Rebolledo, Frederik Rehbach, A.E. Eiben, Thomas Bartz-Beielstein
Series (Serial Number):CIplus (7/2020)
Document Type:Working Paper
Release Date:2020/07/28
Tag:Bayesian Optimization; Benchmarking; Parallelization; Robotics
GND Keyword:Optimierung; Benchmarking; Parallelisierung
Page Number:12
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Data Science, Engineering, and Analytics
CCS-Classification:G. Mathematics of Computing
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
JEL-Classification:C Mathematical and Quantitative Methods
Open Access:Open Access
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International