REVIEW 4 cited by
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
read the original 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.
Forward citations
Cited by 4 Pith papers
-
Fitted Occupancy-Ratio Evaluation without Bellman Completeness
FORE estimates discounted occupancy ratios by iterating KL-projected adjoint Bellman updates, achieving convergence under ratio realizability alone without Bellman completeness.
-
Off-Policy Evaluation for Missingness-Aware Policies in MDPs with Rewards Missing Not at Random
Identifies full-data conditional mean rewards under MNAR missingness via shadow variables and a bridge function, then builds a consistent FQE-style OPE estimator for missingness-aware policies.
-
Autoregressive Diffusion World Models for Off-Policy Evaluation of LLM Agents
ADWM learns a latent diffusion world model with per-transition independent denoising and policy-conditioned guidance to enable accurate offline evaluation of LLM agent policies.
-
MedGym:A Unified Continuous-Time Benchmark for Dynamic Medical Treatment Reinforcement Learning
MedGym introduces a continuous-time RL benchmark for medical treatment derived from clinical data via PINNs, supporting offline/online evaluation on personalization, safety, and discrete vs continuous methods.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.