Bayesian Optimization for Contextual Policy Search
Jan Hendrik Metzen, Alexander Fabisch, Jonas Hansen
In Proceedings of the Second Machine Learning in Planning and Control of Robot Motion Workshop, (IROS MLPC-2015), 02.10.2015, Hamburg, IROS, 2015.

Abstract :

Contextual policy search allows adapting robotic movement primitives to different situations. For instance, a locomotion primitive might be adapted to different terrain inclinations or desired walking speeds. Such an adaptation is often achievable by modifying a relatively small number of hyperparameters; however, learning when performed on an actual robotic system is typically restricted to a relatively small number of trials. In black-box optimization, Bayesian optimization is a popular global search approach for addressing such problems with low-dimensional search space but expensive cost function. We present an extension of Bayesian optimization to contextual policy search. Preliminary results suggest that Bayesian optimization outperforms local search approaches on low-dimensional contextual policy search problems.



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