What is softmax in reinforcement learning?
Ok, I have read a very good explanation to what softmax is.
A dog is programmed to walk through a maze. One start point has very few walls. The way it is programmed with the epsilon greedy algorithm, the dog will sooner or later start to walk mostly to the right or mostly to the left. That is a problem because it at some point might just walk in a circle the entire time. To get that out one could make it choose left or right randomly from time to time - but that is not really a good approach for obvious reasons.
So instead you use something called softmax. Softmax just smoothens the decision process: like taking out the walking-left or walking-right.