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Abstract:
We present a new approach to reinforcement learning in which
the policies considered by the learning process are constrained by
hierarchies of partially specified machines. This allows for the
use of prior knowledge to reduce the search space and provides a
framework in which knowledge can be transferred across problems and
in which component solutions can be recombined to solve larger and
more complicated problems. Our approach can be seen as providing a
link between reinforcement learning and "behavior-based" or
"teleo-reactive" approaches to control. We present provably
convergent algorithms for problem-solving and learning with
hierarchical machines and demonstrate their effectiveness on a
problem with several thousand states.
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