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Deep Active Inference for Partially Observable MDPs
[article]
2020
arXiv
pre-print
Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully observable domains. In this paper, we describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. The deep learning architecture optimizes a variant of the expected free energy and encodes the continuous state representation by means of a variational
arXiv:2009.03622v1
fatcat:ihh4trskzjfqxda4ns5gxnd7wm