My agent uses an $\epsilon$-greedy strategy to learn. The exploration rate (i.e. $\epsilon$) decays throughout the training. I've seen examples where people update $\epsilon$ every time an action is taken, while others update it at the end of the episode. If updated at every action, $\epsilon$ is more continuous. Does it matter? Is there a standard? Is one better than another?