text: We started by defining**A**, or the observation model. In this example, since we allowed 
  for**A**learning,**A**was not used directly, but we still defined it in order to construct the 
  Dirichlet prior over it (and it could be used directly if**A**learning was not required). Of the 
  three observation modalities, the first was the location observation. Here, there were 
  four possible observations, mapped to the four location states and two reward condition 
  states. This resulted in an**A**that was four location observations by four location states by 
  two reward conditions, i.e., a 4 × 4 × 2 tensor. We let the agent correctly assume perfect 
  observations of the location by specifying an identity matrix for**A**in each reward condition: 
  (cid:7)  (cid:4) 
  
  # Use identity matrices between locations and observations . 
  # For reward condition right 
  A[1][:,:,1] = [ 1 .0 
  0 .0 
  0 .0 
  0 .0  0 .0 
  1 .0 
  0 .0 
  0 .0  0 .0 
  0 .0 
  1 .0 
  0 .0  0 .0 
  0 .0 
  0 .0 
  1 .0 ] 