Hey.
My bot seemed to be making wrong prediction after an increase in stories i.e to about 100 stories now. The stories have multiple paths based on slot_was_set to decide which path to take, this slot is a categorical slot. After running rasa run with debug I realized the path taken determined by slot_was_set was being predicted by the TEDPolicy. After an increase in epochs of the TEDPolicy from 100 to 200. The prediction seems to work fine, I also observed a decrease in t_loss in the core model however accuracy remained the same. What is the impact of t_loss on prediction?. Does TEDPolicy require increase in epochs with increasing number of stories?. Despite the prediction improving the training time has exponentially increased.
100 epochs t_loss 0.523 accuracy 1.000 training_time 6 minutes prediction on path to take was wrong
150 epochs t_loss 0.469 accuracy 0.999 training_time 10 minutes
200 epochs t_loss 0.424 accuracy 0.999 training_time 13 minutes
My config,yml file
