text: 2.3. Action in Active Inference 
  
  As with perception, action in AIF is guided by the minimisation of free energy. How- 
  ever, instead of VFE being minimised directly, it is the free energy that is expected to occur 
  depending on the actions taken by the agent—the expected free energy or EFE—that is 
  minimised. As stated below, choosing actions that minimise the EFE leads to a natural 
  balance between exploration and exploitation, ensuring preferences are realised and am- 
  biguity about the environment is minimised. In AIF, policies*π*are sequences of actions 
  u. The policy length (also called the planning horizon or temporal depth) is the length of 
  the policies being considered. The total number of policies therefore depends on the policy 
  length and the number of different actions that can be made at each time step. An EFE is 
  assigned to each policy*π*(denoted as G*π*), where policies associated with a lower EFE are 
  then more likely to be chosen. 
  
  One can rewrite the EFE in different ways to highlight different consequences of 
  optimising it. Below, we show the two most crucial ways to rewrite it, taken from [1,33]. 
  We denote the states and observations that are expected future outcomes of actions with 
  (~). Additionally, we introduce a preference prior**C**that encodes the agent’s preferences: 