diff --git a/scripts/NNDy.py b/scripts/NNDy.py
index 094f6df..23e3873 100644
--- a/scripts/NNDy.py
+++ b/scripts/NNDy.py
@@ -40,6 +40,11 @@ if __name__ == '__main__':
       #if no value is to be set from here globalPar must be an empty dictionary
    globalPar = {}
 
+   # $$$ TRAINING DATASET $$$
+   # if previous run of the same optimization -meaning with the same type of optimizer, same type and number of input parameters- have been performed, the past dataset can be fed to skip the initial training
+   # indicate the path for the learner archive file, to be found in the /M-LOOP_archives/ folder 
+   training_filename = 
+
 
    # $$$ INPUT PARAMETERS $$$
    #indicate variables to be optimized, in the following we will call them "input parameters"
@@ -85,17 +90,25 @@ if __name__ == '__main__':
                                        num_params = num_params, 
                                        min_boundary = min_boundary, max_boundary = max_boundary,
                                        first_params = inputPar,
-                                       param_names = inputPar_names,             
+                                       param_names = inputPar_names,     
+
+                                       #if retrieving dataset from previous runs
+                                       training_filename = training_filename,      
 
                                        #other settings
                                        # %of allowed variation (from 0 to 1) - wrt each parameter range - from current best parameters found, limits the exploration around the current global minimum of the cost function
-                                       trust_region = ,
+                                       trust_region = 1,
                                        #output parameters over which cost is computed are noisy quantities
                                        cost_has_noise = True,
                                        #if False, waits for the experiment to be performed every time so that every new optimization iteration trains on an enlarged training set
-                                       no_delay = False)
-                                    #for other possible settings for the optimizer see documentation https://m-loop.readthedocs.io/en/latest/tutorials.html
+                                       no_delay = False,
+                                       
+                                       default_bad_cost = 0,                  #default cost for bad run
+                                       default_bad_uncertainty = 0,            #default uncertainty for bad run
 
+                                       update_hyperparameters = True         #whether hyperparameters should be tuned to avoid overfitting. Default False.
+                                    #for other possible settings for the optimizer see documentation https://m-loop.readthedocs.io/en/latest/tutorials.html
+   )
 
    #To run M-LOOP and find the optimal parameters just use the controller method optimize
    controller.optimize()