Sparse Additive Off-Policy Evaluation for Reinforcement Learning with Potentially Limited Number of Trajectories
We develop a new framework for flexible, nonlinear, and interpretable off-policy evaluation for infinite-horizon reinforcement learning. To handle large state spaces and support transparent decision-making, we model the Q-function using a nonlinear function class with a sparse additive structure. We derive high-probability finite-sample error bounds for estimating the value function of a target policy and show that the bounds depend only logarithmically on the ambient dimension $d$, thereby alleviating the curse of dimensionality. In contrast to most existing theory for off-policy evaluation, which typically assumes access to many trajectories, our analysis guarantees accurate value estim...
| Authors | Tuoyi Zhao, Chengchun Shi, Zhengling Qi et al. |
|---|---|
| Primary category | stat.ML |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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