Factored Contextual Policy Search with Bayesian optimization

ICRA 2019
Pages: 7242 - 7248
Published: May 20, 2019
Abstract
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitly conditioning the policy on a parametric context space. In this paper, we further structure the contextual policy representation. We propose to factor contexts into two components: target contexts...
Paper Details
Title
Factored Contextual Policy Search with Bayesian optimization
Published Date
May 20, 2019
Journal
Pages
7242 - 7248
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