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