Feudal Reinforcement Learning

Peter Dayan   Geoff Hinton
In NIPS 5, 271-278.


One way to speed up reinforcement learning is to enable learning to happen simultaneously at multiple resolutions in space and time. This paper shows how to create a Q-learning managerial hierarchy in which high level managers learn how to set tasks to their sub-managers who, in turn, learn how to satisfy them. Sub-managers need not initially understand their managers' commands. They simply learn to maximise their reinforcement in the context of the current command.

We illustrate the system using a simple maze task.. As the system learns how to get around, satisfying commands at the multiple levels, it explores more efficiently than standard, flat, Q-learning and builds a more comprehensive map.

compressed postscript   pdf
See also Dayan (1998).

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