AI engineering glossary · Reinforcement learning
What is OPE (off-policy evaluation)?
Off-policy evaluation (also called OPE): Estimating how well a new decision rule would perform using only data logged under a different rule, without deploying it. Methods include importance sampling and doubly robust estimators. It is central to safe personalisation and recommendation.
Off-policy evaluation: an example
Use last month's logs, weighted by probability ratios, to estimate the click rate a new ranking rule would have earned.
Related terms
More Reinforcement learning terms
- Offline reinforcement learning
- On-policy
- Optimal policy
- Options framework
- Overestimation bias
- Partially observable Markov decision process
- Policy evaluation
- Policy gradient theorem
Where you learn it
Off-policy evaluation is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.