  In this work we present a multi-armed bandit framework for online expert
selection in Markov decision processes and demonstrate its use in
high-dimensional settings. Our method takes a set of candidate expert policies
and switches between them to rapidly identify the best performing expert using
a variant of the classical upper confidence bound algorithm, thus ensuring low
regret in the overall performance of the system. This is useful in applications
where several expert policies may be available, and one needs to be selected at
run-time for the underlying environment.
