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Robust Policy Search is the problem of learning policies that do not degradein performance when subject to unseen environment model parameters. It isparticularly relevant for transferring policies learned in a simulationenvironment to the real world. Several existing approaches involve samplinglarge batches of trajectories which reflect the differences in various possibleenvironments, and then selecting some subset of these to learn robust policies,such as the ones that result in the worst performance. We propose an activelearning based framework, EffAcTS, to selectively choose model parameters forthis purpose so as to collect only as much data as necessary to select such asubset. We apply this framework using Linear Bandits, and experimentallyvalidate the gains in sample efficiency and the performance of our approach onstandard continuous control tasks. We also present a Multi-Task Learningperspective to the problem of Robust Policy Search, and draw connections fromour proposed framework to existing work on Multi-Task Learning.

An Active Learning Framework for Efficient Robust Policy Search