David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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forcement learning algorithms that generate only reactive policies and existing probabilistic planning algorithms that requires a substantial amount of a priori knowledge in order to plan we devise a two stage bottom up learning to plan process in which rst reinforcement learn ing dynamic programming is applied without the use of a priori domain speci c knowledge to acquire a reactive policy and then explicit plans are extracted from the learned reactive policy Plan extraction is based on a beam search algorithm that performs temporal projection in a restricted fashion guided by the value functions resulting from reinforcement learn ing dynamic programming..
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