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0
votes
1
answer
580
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large variance from inverse probability weighting (inverse propensity score)
I heard if the observed data that will be used in the inverse probability weighting method is too small, the estimator based on the weighting will have a large variance.
Could you explain why that is …
0
votes
1
answer
64
views
way to transform reinforcement learning problems to bandit problems
I wonder what a general way looks like to transform reinforcement learning problems to bandit problems (especially contextual bandit problems)
Thank you!
1
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0
answers
231
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off-policy evaluation in reinforcement learning
IPS estimator, which is used for off-policy evaluation in a contextual bandit problem, is well explained here: Doubly Robust Policy Evaluation andOptimization https://arxiv.org/pdf/1503.02834.pdf
The …
0
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1
answer
275
views
Policy evaluation in contextual bandit setting
I am currently reading a paper whose links is (Exploration Scavenging) http://delivery.acm.org/10.1145/1400000/1390223/p528-langford.pdf?ip=128.135.98.49&id=1390223&acc=ACTIVE%20SERVICE&key=06A6A3A8AF …
3
votes
1
answer
1k
views
Using IPS(inverse probability weighting) with a deterministic policy as the logging policy
In a contextual bandit problem, why can't we use inverse probability weighting (inverse propensity score) with a deterministic policy as the logging policy? Could you give me a concrete example?
0
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1
answer
102
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Why is having low variance important in offline policy evaluation of reinforcement learning?
Intuitively, I understand that having an unbiased estimate of a policy is important because being biased just means that our estimate is distant from the truth value.
However, I don't understand cle …
0
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1
answer
163
views
quick questions about a contextual bandit problem
I am currently reading the paper "Learning from Logged Implicit Exploration Data" https://arxiv.org/pdf/1003.0120.pdf. But I believe the questions I have can be answered without reading the whole pape …