FeUdal Networks for Hierarchical Reinforcement Learning

@inproceedings{Vezhnevets2017FeUdalNF,
  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},
  booktitle={ICML},
  year={2017}
}
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
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