Settling the sample complexity of model-based offline reinforcement learning Page 1 The
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …

We study the off-policy evaluation (OPE) problem in an infinite-horizon Markov decision
process with continuous states and actions. We recast the $ Q $-function estimation into a …

We consider the off-policy evaluation problem of reinforcement learning using deep
convolutional neural networks. We analyze the deep fitted Q-evaluation method for …

Sample-efficient offline reinforcement learning (RL) with linear function approximation has
been studied extensively recently. Much of the prior work has yielded instance-independent …

Abstract Off-Policy Evaluation (OPE) serves as one of the cornerstones in Reinforcement
Learning (RL). Fitted Q Evaluation (FQE) with various function approximators, especially …

In this paper, we prove state-of-the-art Bayesian regret bounds for Thompson Sampling in
reinforcement learning in a multitude of settings. We present a refined analysis of the …

We study stochastic approximation procedures for approximately solving a $ d $-
dimensional linear fixed point equation based on observing a trajectory of length $ n $ from …

We study optimal procedures for estimating a linear functional based on observational data.
In many problems of this kind, a widely used assumption is strict overlap, ie, uniform …

Offline reinforcement learning (RL) leverages previously collected data for policy
optimization without any further active exploration. Despite the recent interest in this …

We consider the problem of estimating the value function of an infinite-horizon $\gamma $-
discounted Markov reward process (MRP). We establish non-asymptotic guarantees for a …