  Humans instinctively know how to neglect details when it comes to solve
complex decision making problems in environments with unforeseeable variations.
This abstraction process seems to be a vital property for most biological
systems and helps to 'abstract away' unnecessary details and boost
generalisation. In this work we introduce the dispatcher/ executor principle
for the design of multi-task Reinforcement Learning controllers. It suggests to
partition the controller in two entities, one that understands the task (the
dispatcher) and one that computes the controls for the specific device (the
executor) - and to connect these two by a strongly regularizing communication
channel. The core rationale behind this position paper is that changes in
structure and design principles can improve generalisation properties and
drastically enforce data-efficiency. It is in some sense a 'yes, and ...'
response to the current trend of using large neural networks trained on vast
amounts of data and bet on emerging generalisation properties. While we agree
on the power of scaling - in the sense of Sutton's 'bitter lesson' - we will
give some evidence, that considering structure and adding design principles can
be a valuable and critical component in particular when data is not abundant
and infinite, but is a precious resource.
