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- W76178037 abstract "Ve consider reinforcement learning methods for the solution of complex sequential optimization problems. In particular, the soundness of tV'lO methods proposed for the solution of partially obsenrable problems will be shown. The first method suggests a state-estimation scheme and requires mild a priori knowledge, vhile the second method assumes that a significant amount of abstract knowl edge is available about the decision problem and uses this knowledge to setup a macro-hierarchy to turn the partially observable problem into another one which can already be handled using methods worked out for observable problems. This second method is also illustrated with some experiments on a. real-robot." @default.
- W76178037 created "2016-06-24" @default.
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- W76178037 date "2010-01-01" @default.
- W76178037 modified "2023-10-05" @default.
- W76178037 title "Reinforcement Learning: Theory and Practice" @default.
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