What is off-policy evaluation in reinforcement learning?

Off-policy evaluation estimates performance of a policy using data generated by another policy.

OPE in reinforcement learning evaluates a target policy using trajectories generated by a different behavior policy. It uses methods like importance sampling, doubly robust estimators, and model-based simulation. It is crucial for safe evaluation before deployment in real environments.