Feudal Reinforcement Learning
Peter Dayan   Geoff Hinton
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.
In NIPS 5, 271-278.
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).