Comments (7)
In case this link is not accessible to @tawheeler
I am failing to understand the weighting steps in Algorithm 21.9. The relevant steps are
# say w = [1,2,3]
w /= sum(w)
w = [1 - wi for wi in w]
# w would be [0.83, 0.67, 0.5] here
I'm not sure if I understood this correctly, but I believe this would result in a weight vector that sums to
$|S| - 1$ instead of$1$ , which doesn't make sense. May I ask if there might be a typo here?
from decisionmaking.
I think that is indeed a mistake. It should be:
dists = [<L1 distances>]
w = normalize([1/(d+eps) for d in dists], 1)
We can confirm with Kyle if we want to, but this is how kernel smoothing does it (section 8.3).
I'll commit a fix. Thank you!
from decisionmaking.
@Tom-CCC, we typically add contributors to our acknowledgements section. If you would like to be added, could you please share your first and last name?
from decisionmaking.
I'll double check with Kyle real quick.
from decisionmaking.
Sure, My name is Shengtong Zhang.
from decisionmaking.
Great! We're still digging into this. The reference implementation is here:
https://github.com/trey0/zmdp/blob/master/src/pomdpBounds/SawtoothUpperBound.cc#L46
from decisionmaking.
We have updated the implementation in Alg. 21.9 and reposted the PDF and acknowledged you. We also streamlined the text in Sec. 21.6. Thank you for bringing this to our attention!
from decisionmaking.
Related Issues (20)
- 4 Parameter Learning. 4.1 Maximum Likelihood Parameter Learning HOT 2
- Norm in SetCategorical HOT 3
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- Example 22.5 - Incorrect observation used in the second update HOT 1
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from decisionmaking.