Active Learning in Partially Observable Markov Decision Processes (original) (raw)

Doina Precup

2005

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2 pages

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Learning in Partially Observable Markov Decision Processes is a notoriously difficult problem. The goal of our research is to address this problem for environments in which a partial model may be available, in the beginning, but in which there is uncertainty about the model parameters. We developed and algorithm called MEDUSA , which is based on ideas from active learning [1,2,3].