text: (cid:7) 
  scaling_parameter = 2 .0 
  
  pA = deepcopy (A)*scaling_parameter # Create pA as a scaled copy of A 
  
  pB = deepcopy (B)*scaling_parameter # Create pB as a scaled copy of B 
  
  pD = deepcopy (D)*scaling_parameter # Create pD as a scaled copy of D 
  (cid:6)  (cid:5) 
  
  Finally, we define the hyper-parameters and other settings in two dictionaries. Here, we 
  display the most common parameters and settings, and set them to their default values, 
  which are used if they are not set by the user: 
  (cid:7)  (cid:4) 
  parameters = Dict ( 
  
  " alpha " => 16 .0 , # Action precision alpha 
  " gamma " => 16 .0 , # Policy precision gamma 
  " lr_pA " => 1 .0 , 
  " fr_pA " => 1 .0 , 
  " lr_pB " => 1 .0 , 
  " fr_pB " => 1 .0 , 
  " lr_pD " => 1 .0 , 
  " fr_pD " => 1 .0 
  ) 
  
  settings = Dict ( 
  
  # Length of policies to consider 
  " policy_len " 
  # Whether to use state information gain for action selection 
  " use_states_info_gain " => true , 
  # Whether to use parameter information gain for action selection 
  " use_param_info_gain " 
  # Whether to use pragmatic gain for action selection 
  " use_utility " 
  ) 
  (cid:6) 