FeUdal Networks for Hierarchical Reinforcement Learning

  title={FeUdal Networks for Hierarchical Reinforcement Learning},
  author={Alexander Sasha Vezhnevets and Simon Osindero and Tom Schaul and Nicolas Heess and Max Jaderberg and David Silver and Koray Kavukcuoglu},
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… CONTINUE READING
Highly Cited
This paper has 151 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 99 extracted citations

151 Citations

Citations per Year
Semantic Scholar estimates that this publication has 151 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 35 references

The Option-Critic Architecture

View 9 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…