Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
Abstract
Off-policy policy evaluation (OPE) is the problem of estimating the online performance of a policy using only pre-collected historical data generated by another policy. Given the increasing interest in deploying learning-based methods for safety-critical applications, many recent OPE methods have recently been proposed. Due to disparate experimental conditions from recent literature, the relative performance of current OPE methods is not well understood. In this work, we present the first comprehensive empirical analysis of a broad suite of OPE methods. Based on thousands of experiments and detailed empirical analyses, we offer a summarized set of guidelines for effectively using OPE in practice, and suggest directions for future research.
Attached Files
Submitted - 1911.06854.pdf
Files
Name | Size | Download all |
---|---|---|
md5:84ff2c8cdec5af2764ebec14fa3df3c2
|
2.7 MB | Preview Download |
Additional details
- Eprint ID
- 100590
- Resolver ID
- CaltechAUTHORS:20200109-100747650
- Created
-
2020-01-09Created from EPrint's datestamp field
- Updated
-
2023-06-02Created from EPrint's last_modified field