Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning

Understand

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.