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

We study the\emph {offline reinforcement learning}(offline RL) problem, where the goal is to
learn a reward-maximizing policy in an unknown\emph {Markov Decision Process}(MDP) …

We consider a challenging theoretical problem in offline reinforcement learning (RL):
obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under …

Abstract We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new
practical algorithm for offline reinforcement learning (RL) in complex environments with …

We seek to understand what facilitates sample-efficient learning from historical datasets for
sequential decision-making, a problem that is popularly known as offline reinforcement …

Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong
assumptions on both the function classes (eg, Bellman-completeness) and the data …

Offline reinforcement learning (RL), where the agent aims to learn the optimal policy based
on the data collected by a behavior policy, has attracted increasing attention in recent years …

This paper presents a systematic study on gap-dependent sample complexity in offline
reinforcement learning. Prior works showed when the density ratio between an optimal …

Offline 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, two …

In offline reinforcement learning (RL), a learner leverages prior logged data to learn a good
policy without interacting with the environment. A major challenge in applying such methods …