Learning to select computations

Understand

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.