text: q( ˜s|*π*)[H(p( ˜o| ˜s))] 
  (cid:125) 
  (cid:123)(cid:122) 
  Expected ambiguity 
  + DKL[q( ˜o|*π*)∥p( ˜o|C)] 
  (cid:125) 
  (cid:123)(cid:122) 
  Risk (outcomes) 
  (cid:124) 
  
  Here, the first term on the right-hand side captures the expected entropy, or uncertainty, 
  of the outcomes given the environmental states. Minimising this quantity ensures that 
  the agent will seek states where observations can most clearly be used to distinguish 
  between environmental states. The second term is the KL divergence of the expected 
  observations from preferred observations, capturing the risk of making unwanted (i.e., a 
  priori surprising) observations, which is also minimised by minimising the EFE. 
  
  2.4. Learning in Active Inference 
  
  In AIF, the parameters of the generative model can also be updated via Bayesian-belief- 
  updating methods, a process called “parameter learning” or sometimes just “learning” [2]. 
  In general, this is performed by introducing belief distributions over the possible values 
  of the parameters that are subject to learning, and updating this distribution for each 
  observation using Bayesian belief updating. This additionally implies introducing priors 
  on the belief distributions. Depending on the type of generative model used, the belief 
  distributions and their priors will take different forms, and so will their update equations. 
  In the following, we demonstrate parameter learning specifically in the context of POMDPs. 
  The parameters that are subject to learning in POMDPs are usually the entries in the 
  five matrices. Since the matrices consist of categorical probability distributions, it is natural 
  to use Dirichlet distributions—distributions over categorical probability distributions—as 
  belief distributions over their values [33,52]. Beliefs about each probability distribution 