Learning to select computations
  The efficient use of limited computational resources is an essential
ingredient of intelligence. Selecting computations optimally according to
rational metareasoning would achieve this, but this is computationally
intractable. Inspired by psychology and neuroscience, we propose the first
concrete and domain-general learning algorithm for approximating the optimal
selection of computations: Bayesian metalevel policy search (BMPS). We derive
this general, sample-efficient search algorithm for a computation-selecting
metalevel policy based on the insight that the value of information lies
between the myopic value of information and the value of perfect information.
We evaluate BMPS on three increasingly difficult metareasoning problems: when
to terminate computation, how to allocate computation between competing
options, and planning. Across all three domains, BMPS achieved near-optimal
performance and compared favorably to previously proposed metareasoning
heuristics. Finally, we demonstrate the practical utility of BMPS in an
emergency management scenario, even accounting for the overhead of
metareasoning.
