I'm taking a Coursera course on Reinforcement learning. There was a question there that wasn't addressed in the learning material: Does adding a constant to all rewards change the set of optimal policies in episodic tasks?
The answer is Yes - Adding a constant to the reward signal can make longer episodes more or less advantageous (depending on whether the constant is positive or negative).
Can anyone explain why is this so? And why it doesn't change in the case of continuous (non episodic) tasks? I don't see why adding a constant matters - as an optimal policy would still want to get the maximum reward...
Can anyone give an example of this?