text: # Make a scaled copy of A 
  pA = deepcopy (A)*scale_concentration_parameter 
  (cid:6)  (cid:5) 
  
  We then set the various hyper-parameters for the agent’s inference algorithm. Here, we 
  used standard default values: an**A**learning rate of 1, and*γ*and*α*values of 16: 
  (cid:7)  (cid:4) 
  parameters = Dict ( 
  
  " lr_pA " => 1 .0 , 
  " alpha " => 16 .0 , 
  " gamma " => 16 .0 , 
  
  ) 
  (cid:6)  (cid:5) 
  
  Finally, we defined the settings of the agent (other characteristics of the agent that were not 
  estimable parameters). Here, we set the policy length to 2, which let the agent use expected 
  information gain for both the state and parameter info gain for its actions, and specify that 
  it was specifically the second modality of**A**that was to be learned: 
  (cid:7)  (cid:4) 
  settings = Dict ( 
  
  # A policy length of two 
  " policy_len " => 2, 
  
  # Use parameter information gain in action selection 
  " use_param_info_gain " => true , 