Feature Dynamic Bayesian Networks

نویسنده

  • Marcus Hutter
چکیده

Feature Markov Decision Processes (ΦMDPs) [Hut09] are well-suited for learning agents in general environments. Nevertheless, unstructured (Φ)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale realworld problems. In this article I extend ΦMDP to ΦDBN. The primary contribution is to derive a cost criterion that allows to automatically extract the most relevant features from the environment, leading to the “best” DBN representation. I discuss all building blocks required for a complete general

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عنوان ژورنال:
  • CoRR

دوره abs/0812.4581  شماره 

صفحات  -

تاریخ انتشار 2008