Factored Contextual Policy Search with Bayesian Optimization
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 "contexts". Bayesian optimization approaches to contextual policy search (CPS) offer data-efficient policy learning that generalize over a context space. We propose to improve data-efficiency by factoring typically considered contexts into two components: target-type contexts that correspond to a desired outcome of the learned behavior, e.g. target position for throwing a ball; and environment type contexts that correspond to some state of the environment, e.g. initial ball position or wind speed. Our key observation is that experience can be directly generalized over target-type contexts. Based on that we introduce Factored Contextual Policy Search with Bayesian Optimization for both passive and active learning settings. Preliminary results show faster policy generalization on a simulated toy problem. A full paper extension is available at arXiv:1904.11761
Cite
@article{arxiv.1612.01746,
title = {Factored Contextual Policy Search with Bayesian Optimization},
author = {Peter Karkus and Andras Kupcsik and David Hsu and Wee Sun Lee},
journal= {arXiv preprint arXiv:1612.01746},
year = {2019}
}
Comments
BayesOpt 2016, NeurIPS Workshop. A full paper extension is available at arXiv:1904.11761