Empirical Study of Off-Policy Policy Evaluation for Reinforcement LearningDownload PDF

08 Jun 2021, 08:04 (edited 07 Nov 2021)NeurIPS 2021 Datasets and Benchmarks Track (Round 1)Readers: Everyone
  • Keywords: reinforcement learning, off-policy evaluation, benchmark, OPE, RL, off-policy policy evaluation, empirical study
  • TL;DR: We offer an experimental benchmark and empirical study for off-policy policy evaluation in reinforcement learning
  • Abstract: We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications. Given the increasing interest in deploying learning-based methods, there has been a flurry of recent proposals for OPE method, leading to a need for standardized empirical analyses. Our work takes a strong focus on diversity of experimental design to enable stress testing of OPE methods. We provide a comprehensive benchmarking suite to study the interplay of different attributes on method performance. We distill the results into a summarized set of guidelines for OPE in practice. Our software package, the Caltech OPE Benchmarking Suite (COBS), is open-sourced and we invite interested researchers to further contribute to the benchmark.
  • Supplementary Material: zip
  • URL: https://github.com/clvoloshin/COBS
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