# How to correctly discount actor critic with experience replay?

In my related question, I asked about the one step actor critic from The Reinforcement Learning Book by Richard Sutton et al, section 13.5:

The learning is becoming less significant as the episode progresses, by means of making $$I$$ smaller by the discounting factor, as the episode progresses.

How would this discounting generalize to experience replay?

Meaning, if we want to update $$\theta$$ and $$w$$ by some experience e=$$(s, a, r, s')$$, for which we don't know by how much to discount, how would we accomplish a correct update?

Should we remember the discount amount $$I$$ in the experience?

Please note the critic here is different from the critic here, because it estimates the state-value function $$V(s)$$, rather than the action-state-value function $$Q(s,a)$$