Bottom-up learning of hierarchical models in a class of deterministic POMDP environments - Itoh, Hideaki; Fukumoto, Hisao; Wakuya, Hiroshi; Furukawa, Tatsuya (Zielona Góra: Uniwersytet Zielonogórski)
The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks.
Keywords: partially observable Markov decision processes; hierarchical models; bottom-up learning