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.
arXiv preprint arXiv:1911.06854 , year=
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UNVERDICTED 3representative citing papers
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 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.
citing papers explorer
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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.
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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.
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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.