A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

Offline or batch reinforcement learning seeks to learn a near-optimal policy using history
data without active exploration of the environment. To counter the insufficient coverage and …

We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …

We study reinforcement learning (RL) with linear function approximation. For episodic time-
inhomogeneous linear Markov decision processes (linear MDPs) whose transition …

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\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) …

Coverage conditions--which assert that the data logging distribution adequately covers the
state space--play a fundamental role in determining the sample complexity of offline …

Reward-free reinforcement learning (RL) considers the setting where the agent does not
have access to a reward function during exploration, but must propose a near-optimal policy …

Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing only
previously collected experience, without any online interaction. While it is widely understood …

We study time-inhomogeneous episodic reinforcement learning (RL) under general function
approximation and sparse rewards. We design a new algorithm, Variance-weighted …