  Latest insights from biology show that intelligence not only emerges from the
connections between neurons but that individual neurons shoulder more
computational responsibility than previously anticipated. This perspective
should be critical in the context of constantly changing distinct reinforcement
learning environments, yet current approaches still primarily employ static
activation functions. In this work, we motivate why rationals are suitable for
adaptable activation functions and why their inclusion into neural networks is
crucial. Inspired by recurrence in residual networks, we derive a condition
under which rational units are closed under residual connections and formulate
a naturally regularised version: the recurrent-rational. We demonstrate that
equipping popular algorithms with (recurrent-)rational activations leads to
consistent improvements on Atari games, especially turning simple DQN into a
solid approach, competitive to DDQN and Rainbow.
