Reinforcement learning: Discounting rewards in the REINFORCE algorithm

Question

I am looking into the REINFORCE algorithm for reinforcement learning. I am having trouble understanding how rewards should be computed.

The algorithm from Sutton & Barto:

What does G, 'return from step t' mean here?

1. Return from step t to step T-1, i.e. R_t + R_(t+1) + ... + R_(T-1)?
2. Return from step 0 to step t?, i.e. R_0 + R_1 + ... + R_(t)?