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arXiv · 2009.03622

Deep Active Inference for Partially Observable MDPs

Otto van der Himst·Pablo Lanillos
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Abstract

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 autoencoder. 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.

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BibTeXRIS

Otto van der Himst, Pablo Lanillos. 2020-09-08. Deep Active Inference for Partially Observable MDPs. https://doi.org/10.1007/978-3-030-64919-7_8

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