Deep active inference agents using Monte-Carlo methods

• URL: http://arxiv.org/abs/2006.04176v2
• Date: Thu, 22 Oct 2020 13:17:36 GMT
• Title: Deep active inference agents using Monte-Carlo methods
• Authors: Zafeirios Fountas, Noor Sajid, Pedro A.M. Mediano, Karl Friston
• Abstract summary: We present a neural architecture for building deep active inference agents in continuous state-spaces using Monte-Carlo sampling. Our approach enables agents to learn environmental dynamics efficiently, while maintaining task performance. Results show that deep active inference provides a flexible framework to develop biologically-inspired intelligent agents.
• Score: 3.8233569758620054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active inference is a Bayesian framework for understanding biological intelligence. The underlying theory brings together perception and action under one single imperative: minimizing free energy. However, despite its theoretical utility in explaining intelligence, computational implementations have been restricted to low-dimensional and idealized situations. In this paper, we present a neural architecture for building deep active inference agents operating in complex, continuous state-spaces using multiple forms of Monte-Carlo (MC) sampling. For this, we introduce a number of techniques, novel to active inference. These include: i) selecting free-energy-optimal policies via MC tree search, ii) approximating this optimal policy distribution via a feed-forward `habitual' network, iii) predicting future parameter belief updates using MC dropouts and, finally, iv) optimizing state transition precision (a high-end form of attention). Our approach enables agents to learn environmental dynamics efficiently, while maintaining task performance, in relation to reward-based counterparts. We illustrate this in a new toy environment, based on the dSprites data-set, and demonstrate that active inference agents automatically create disentangled representations that are apt for modeling state transitions. In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i.e., plan), to evince reward-directed navigation - despite temporary suspension of visual input. These results show that deep active inference - equipped with MC methods - provides a flexible framework to develop biologically-inspired intelligent agents, with applications in both machine learning and cognitive science.

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