Feature Dynamic Bayesian Networks
Marcus Hütter · 2009
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 learning algorithm.