  Deep neural network (DNN) based approaches hold significant potential for
reinforcement learning (RL) and have already shown remarkable gains over
state-of-art methods in a number of applications. The effectiveness of DNN
methods can be attributed to leveraging the abundance of supervised data to
learn value functions, Q-functions, and policy function approximations without
the need for feature engineering. Nevertheless, the deployment of DNN-based
predictors with very deep architectures can pose an issue due to computational
and other resource constraints at test-time in a number of applications. We
propose a novel approach for reducing the average latency by learning a
computationally efficient gating function that is capable of recognizing states
in a sequential decision process for which policy prescriptions of a shallow
network suffices and deeper layers of the DNN have little marginal utility. The
overall system is adaptive in that it dynamically switches control actions
based on state-estimates in order to reduce average latency without sacrificing
terminal performance. We experiment with a number of alternative loss-functions
to train gating functions and shallow policies and show that in a number of
applications a speed-up of up to almost 5X can be obtained with little loss in
performance.
