text: (cid:7) 
  
  # For the location state factor 
  D[1] = [1 .0 , 0 .0 , 0 .0 , 0 .0 ] # Certain beleif of being at center location 
  
  # For the reward condition state factor 
  D[2] = [0 .5 , 0 .5 ] # Agnostic prior over reward condition state 
  (cid:6)  (cid:5) 
  
  Finally, we established**E**, the prior over policies. This was a vector over all possible policies; 
  with four possible actions and a policy length of 2, there were 42 = 16 policies in total. 
  Here, we gave the agent an agnostic prior over policies: 
  (cid:7)  (cid:4) 
  
  # Setting uniform E vector to not prefer any policies by equally preferring all 
  E .= 1 .0 / length (E) 
  (cid:6)  (cid:5) 
  
  Thus, the five matrices were defined (we note that the C, D and E matrices were set to 
  agnostic by default if not defined by the user). All that was left was to set the Dirichlet 
  prior for**A**learning, and set the parameters and settings. In the following, we set the the 
  Dirichlet prior for**A**to a scaled copy of the original**A**using a weak scaling parameter 
  of 2.0: 
  (cid:7)  (cid:4) 
  
  # Use a weak scaling parameter 
  scale_concentration_parameter = 2 .0 