Adaptive resource allocation in self-reflective learning agents

Abstract

The computational inefficiency of modern neural networks, which apply uniform processing to inputs of varying complexity, is examined in this paper. An adaptive reinforcement learning agent that dynamically regulates its own computational effort based on the assessed difficulty of each observation is proposed. Special attention is given to the energy efficiency of the approach and its ability to maintain decision quality comparable to fixed-depth baselines.