Today I read a paper titled “Feature Dynamic Bayesian Networks”
The abstract is:
Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments.
Nevertheless, unstructured (Phi)MDPs are limited to relatively simple environments.
Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems.
In this article I extend PhiMDP to PhiDBN.
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.