arXiv:2506. 00818v2 Announce Type: replace-cross Abstract: Offline reinforcement learning for longitudinal studies often faces two linked challenges: rewards may be binary or bounded, and reward observations may be available only for a subset of trajectories or time points even when the corresponding state-action-next-state histories are available.
By Sinian Zhang, Kaicheng Zhang, Ziping Xu, Zongqi Xia, Jue Hou, Tianxi Cai, Doudou Zhou
arXiv:2607. 14346v1 Announce Type: new Abstract: Policy learning methods are increasingly used to inform treatment allocation under budget constraints.
By Johnna Sundberg, Rayid Ghani, Eli Ben-Michael, Edward Kennedy
The paper introduces a model-based bootstrap framework for uncertainty quantification in offline policy evaluation (OPE) within finite-horizon, time-inhomogeneous Markov decision processes. Unlike traditional bootstrap methods that resample entire episodes, this approach regenerates trajectories from an estimated MDP, enabling use of diverse offline data formats such as complete trajectories, transition-level observations, and trajectory fragments. The authors prove bootstrap distributional consistency, asymptotically valid confidence intervals, and consistent variance estimation, and demonstrate through simulations that the method yields tighter confidence intervals and more accurate variance estimates compared to existing techniques.
By Weiwei Wang, Yuqiang Li, Xianyi Wu, Bingyi Jing
arXiv:2501.06926v5 Announce Type: replace
Abstract: Double reinforcement learning (DRL) provides efficient off-policy inference for policy values in nonparametric Markov decision processes (MDPs), bu...
By Lars van der Laan, David Hubbard, Allen Tran, Nathan Kallus, Aur\'{e}lien Bibaut
arXiv:2602. 03778v2 Announce Type: replace-cross Abstract: Tail-end risk measures such as static conditional value-at-risk (CVaR) are used in safety-critical applications to prevent rare, yet catastrophic events.
By Aneri Muni, Vincent Taboga, Esther Derman, Pierre-Luc Bacon, Erick Delage
The paper introduces state abstractions that preserve the difference of Q‑functions for offline reinforcement learning, aiming to exclude irrelevant dynamics from rich state data. It proposes a dynamic generalization of the R‑learner that uses orthogonal estimation and sparse learning to estimate the Q‑function contrast, achieving faster convergence and consistency under a margin condition. Experiments on simulated and simulator‑augmented real data show variance reductions and demonstrate that the necessary information for sequential decision‑making can be smaller than that required for full state prediction.
By Defu Cao, Angela Zhou