Reinforcement Learning - Return Function

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The discussion centers on the use of the index "k" in summation, clarifying its role as a dummy index distinct from "t," which indicates the first term in the summation. The necessity of using a new index like "k" arises because summing over "t" would lead to confusion, as "t" would be replaced and disappear during the expansion of the sum. The explanation emphasizes that "k" is chosen to avoid this issue, ensuring clarity in the summation process.
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In this post on the Return function is indexed by k?
1576126497380.png

Where did the k come from? I was expecting the index to be t.
 
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##t## tells you the index of the first term you are summing over, so you can't sum over that. You need to sum over a new index, called a dummy index - they've chosen ##k##. Note that if you expand the sum, you will replace ##k## with 0 in the zeroth term, replace it with 1 in the first term, and so on - ##k## disappears when you expand the sum. If you'd summed over ##t##, ##t## would likewise be replaced and disappear when you expanded sum, which is not what you want.
 

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