Deep Active Inference for Partially Observable MDPs

• URL: http://arxiv.org/abs/2009.03622v1
• Date: Tue, 8 Sep 2020 10:02:40 GMT
• Title: Deep Active Inference for Partially Observable MDPs
• Abstract summary: We describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. We show, in the OpenAI benchmark, that our approach has comparable or better performance than deep Q-learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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