In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

Offline (or batch) reinforcement learning (RL) algorithms seek to learn an optimal policy from
a fixed dataset without active data collection. Based on the composition of the offline dataset …

Offline reinforcement learning (RL) aims to find performant policies from logged data without
further environment interaction. Model-based algorithms, which learn a model of the …

In many areas, practitioners seek to use observational data to learn a treatment assignment
policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or …

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 …

We consider the offline reinforcement learning problem, where the aim is to learn a decision
making policy from logged data. Offline RL--particularly when coupled with (value) function …

A treatment regime is a sequence of decision rules, one per decision point, that maps
accumulated patient information to a recommended intervention. An optimal treatment …

When observed decisions depend only on observed features, off-policy policy evaluation
(OPE) methods for sequential decision problems can estimate the performance of evaluation …

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

This paper addresses the problem of policy selection in domains with abundant logged data,
but with a restricted interaction budget. Solving this problem would enable safe evaluation …