Deep Active Inference for Partially Observable MDPs [article]

Otto van der Himst, Pablo Lanillos
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
more » ... der. We show, in the OpenAI benchmark, that our approach has comparable or better performance than deep Q-learning, a state-of-the-art deep reinforcement learning algorithm.
arXiv:2009.03622v1 fatcat:ihh4trskzjfqxda4ns5gxnd7wm