text: # D will be : [1 , 0, 0, 0 , 0, 0] 
  D[1] = onehot (1,6)  # Initialize the single element of the D object with a one - hot vector 
  
  # To make the agent prefer policy 2 
  E = onehot (2,2) # Initialize as a one - hot encoded vector : [0 ,1] 
  (cid:6)  (cid:5) 
  
  We now create the Dirichlet priors for**A**,**B**and**D**. When we use parameter learning, these 
  are used to define**A**,**B**and**D**defined above, and are updated at every time step. One 
  way to construct Dirichlet priors is to simply multiply the matrices below with a scaling 
  factor; a higher scaling leads to more precise priors that require stronger evidence to update. 
  Here, we use a scaling parameter of 2. In the current version, parameter learning is only 
  implemented for the**A**,**B**and**D**: 