# Confusion about the derivation of the TD-Learning update rule

I am currently trying to understand the paper "Learning to Predict by the Methods of Temporal Differences" by Sutton. I am stuck with the following step:

(From "Learning to Predict by the Methods of Temporal Differences" by Sutton, p. 14) I do not understand why the equality should still hold after switching the indices k and t and changing the inner sum to go from the beginning to the current temporal difference k instead of running over all temporal differences from t to the end. How is this transformation possible and are there any special assumptions involved for this to hold?

Edit: If I get it right, in essence this is all about proving: $$\sum^m_{t=1}\sum^m_{k=t}f(k,t) = \sum^m_{k=1}\sum^k_{t=1}f(k,t)$$

I would be grateful for hints on how to accomplish that

Okay, I think I found a solution: As the order of summation does not matter, we can write it in set-form: $$\sum^m_{t=1} ... = \sum_{t'} \forall t' \le m$$
$$\sum^m_{t=1}\sum^m_{k=t} f(k,t)=\sum_{t'}\sum_{k'}f(k,t)=\sum_{t',k'}f(k,t), \forall t' \le m, k' \ge t', k' \le m$$
$$\sum^m_{k=1}\sum^k_{t=1}f(k,t) = \sum_{k'}\sum_{t'}f(k,t)=\sum_{k',t'}f(k,t) \forall k' \le m, t' \le k'$$
The sets that the sum is built over are equal because $$k' \le m, t' \le k' \implies t' \le m$$