In the original prioritized experience replay paper, the authors track $\gamma_t$ in every state transition tuple (see line 6 in algorithm below):
Why do the authors track this at every time step? Also, many blog posts and implementations leave this out (including I believe the OpenAI implementation on github).
Can someone explain explicitly how $\gamma_t$ is used in this algorithm?
Note: I understand the typical use of $\gamma$ as a discount factor. But typically gamma remains fixed. Which is why I’m curious as to the need to track it.