  We consider convolutional neural network (CNN) ensemble learning with the
objective function inspired by least action principle; it includes resource
consumption component. We teach an agent to perceive images through the set of
pre-trained classifiers and want the resulting dynamically configured system to
unfold the computational graph with the trajectory that refers to the minimal
number of operations and maximal expected accuracy. The proposed agent's
architecture implicitly approximates the required classifier selection function
with the help of reinforcement learning. Our experimental results prove, that
if the agent exploits the dynamic (and context-dependent) structure of
computations, it outperforms conventional ensemble learning.
