Local Bayesian optimization of motor skills

Akrour, R., Sorokin, D., Peters, J. and Neumann, G. (2017) Local Bayesian optimization of motor skills. In: International Conference on Machine Learning (ICML), 6 - 11 August 2017, Sydney, Australia.

lbayes_motor_skills.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Bayesian optimization is renowned for its sample
efficiency but its application to higher dimensional
tasks is impeded by its focus on global
optimization. To scale to higher dimensional
problems, we leverage the sample efficiency of
Bayesian optimization in a local context. The
optimization of the acquisition function is restricted
to the vicinity of a Gaussian search distribution
which is moved towards high value areas
of the objective. The proposed informationtheoretic
update of the search distribution results
in a Bayesian interpretation of local stochastic
search: the search distribution encodes prior
knowledge on the optimum’s location and is
weighted at each iteration by the likelihood of
this location’s optimality. We demonstrate the
effectiveness of our algorithm on several benchmark
objective functions as well as a continuous
robotic task in which an informative prior is obtained
by imitation learning.

Keywords:Bayesian Optimization, Stochastic Search, Local Search
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:27902
Deposited On:18 Jul 2017 14:57

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