  We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical
reinforcement learning. Our approach is inspired by the feudal reinforcement
learning proposal of Dayan and Hinton, and gains power and efficacy by
decoupling end-to-end learning across multiple levels -- allowing it to utilise
different resolutions of time. Our framework employs a Manager module and a
Worker module. The Manager operates at a lower temporal resolution and sets
abstract goals which are conveyed to and enacted by the Worker. The Worker
generates primitive actions at every tick of the environment. The decoupled
structure of FuN conveys several benefits -- in addition to facilitating very
long timescale credit assignment it also encourages the emergence of
sub-policies associated with different goals set by the Manager. These
properties allow FuN to dramatically outperform a strong baseline agent on
tasks that involve long-term credit assignment or memorisation. We demonstrate
the performance of our proposed system on a range of tasks from the ATARI suite
and also from a 3D DeepMind Lab environment.
