text: minimisation [9]. Choosing actions that minimise the expected free energy (EFE) of their 
  consequences provides a natural balance between exploratory and exploitative behaviour; 
  generalises descriptive approaches to behavioural modelling, like reinforcement learning 
  and expected utility maximisation; and provides a singular approach to adaptive behaviour 
  that can be used across different environments. AIF was argued to be applicable to any self- 
  organising system that actively maintains a stable boundary that defines its integrity [10], 
  a broad category that includes cells and plants [11], as well as humans [2] and even 
  collectives [12]. Owing to its generality, AIF has seen a rise in popularity across multiple 
  fields. It is used for theoretical simulations of the mechanisms underlying various types of 
  behaviour [2], computational phenotyping in computational psychiatry [13,14], and agent- 
  based simulations of population dynamics [15], as well as in engineering and robotics [16]. 
  In AIF, perception and concurrent action are based on performing a variational Bayesian 
  inversion of a generative model of the environment (i.e., a model of how the environment 
  changes and brings about sensory observations). This belief updating includes inferring 
  (hidden) states of the environment, learning parameters of the generative model and 
  learning the structure of the generative model. Since the requisite inference schemes come 
  pre-specified, the main task in AIF modelling becomes specifying an appropriate generative 
  model. This includes specifying priors over environmental states, as well as what might 
  be called prior preferences, preference priors or goal priors: immutable prior expectations that 
  make up an agents’ preferences by furnishing a set of predictions over future states or 
  observations; in fulfilling these predictions, free energy is minimised. The space of possible 
  generative models is vast, and they often have to be handcrafted for a given environment. 
  However, there are some families of generative models that can be considered “universal” 
  in the sense that they can be used for most environments. Currently, the most popular of 
  these is the discrete state-space Partially Observable Markov Decision Process (POMDP)- 
  based generative models. Since they are ubiquitous in the literature, we focus here on 
  making these types of generative models available to researchers. There are, however, other 
  types of universal generative models, like generalised filtering models [17] or Hierarchical 
  Gaussian Filtering-based models [18,19], that will be implemented in the future. 