I am currently implementing the paper Active Object Localization with Deep Reinforcement Learning in Python. While reading about the reward scheme I came across the following:
Finally, the proposed reward scheme implicitly considers the number of steps as a cost because of the way in which Q-learning models the discount of future rewards (positive and negative).
How would you implement this "number of steps" cost? I am keeping track of the number of steps that have been taken, therefore would it be best to use an exponential functions to discount the reward at the current time step?
If anyone has a good idea or knows the standard in regard to this I would love to hear your thoughts.