text: **2. Active Inference with POMDPs**
  
  In this section, we briefly describe the core concepts of AIF and POMDPs. This 
  should familiarise the reader with the vernacular used in the later sections regarding 
  the functionalities of the package. While various extensions, such as structure learning, 
  which enables an agent to learn the structure or shape of its environment through model 
  comparison [44–47], or hierarchical and temporally deep POMDPs [48,49], are relevant for 
  future work, describing these in detail is beyond the scope of this foundational paper. 
  
  At the core of AIF lies the minimisation of a variational free energy upper bound on 
  surprise for perception, as well as action. This is motivated by the free energy principle [4–8], 
  which states that self-organising systems can be described as minimising the variational 
  free energy of their sensory states. The minimisation of free energy generally takes two 