In Reinforcement Learning (RL), we specify the reward function so that the agent can learn the optimal policy. This reward function can come in various forms. It can be a scalar, a function, or anything else.
In the problem of Inverse Reinforcement Learning (IRL), the reverse happens: we are trying to find the reward function that can explain an expert's/demonstrator's behaviour.
My question is, upon finding this reward function, in what form does the reward function appear? Is it a scalar? A Function? or What?
I am trying to learn IRL, looking at some codes in the internet that demonstrate this concept. So far I am not really sure what the output is and how the reward looks like.
Your insights are appreciated.