Introducing <tt>ActiveInference.jl</tt>: A Julia Library for Simulation and Parameter Estimation with Active Inference Models

We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp lib...

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Bibliographic Details
Main Authors: Samuel William Nehrer, Jonathan Ehrenreich Laursen, Conor Heins, Karl Friston, Christoph Mathys, Peter Thestrup Waade
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Entropy
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Online Access:https://www.mdpi.com/1099-4300/27/1/62
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Summary:We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python. ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive and behavioural modelling, as it is used in computational psychiatry, cognitive science and neuroscience. This means that POMDP active inference models can now be easily fit to empirically observed behaviour using sampling, as well as variational methods. In this article, we show how ActiveInference.jl makes building POMDP active inference models straightforward, and how it enables researchers to use them for simulation, as well as fitting them to data or performing a model comparison.
ISSN:1099-4300