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Quantitative Finance · Glosarium

Apa itu Off-policy evaluation, doubly robust estimator?

Dikenal juga sebagai: off-policy evaluation · doubly robust estimator

Definition 17.6 Machine Learning for Markets · Bab 17 — Reinforcement Learning Foundations

Off-policy evaluation estimates the value of a target policy from episodes generated by another, the behaviour policy, typically by reweighting each episode by the ratio of the two policies’ probabilities of the actions taken (importance sampling, Book 4, chapter 26). The doubly robust estimator adds to a model’s estimate of the target’s value the importance-weighted errors of the model on the logged rewards; it is unbiased if either the probabilities or the model are right, and has lower variance than importance sampling when the model is close (Dudík, Langford and Li, 2011).

estimatorbiasstandard deviationroot mean square error
per-decision importance sampling0.201.751.76
weighted importance sampling0.490.630.80
model alone0.3000.30
doubly robust−0.03-0.030.290.29
Table 17.1. Estimates of the target policy’s cost (true value 2.77 bp per lot) from 300 logged episodes of the desk’s behaviour policy, over 100 repetitions (bp per lot). Data: ml_rl.off_policy.
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