Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on
a dataset collected a priori. Due to the lack of further interactions with the environment …

The use of pessimism, when reasoning about datasets lacking exhaustive exploration has
recently gained prominence in offline reinforcement learning. Despite the robustness it adds …

Actor-critic methods are widely used in offline reinforcement learningpractice, but are not so
well-understood theoretically. We propose a newoffline actor-critic algorithm that naturally …

Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-
critic approach involving off-policy evaluation. In this paper we show that simply doing one …

We study model-based offline Reinforcement Learning with general function approximation
without a full coverage assumption on the offline data distribution. We present an algorithm …

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) …

Offline reinforcement learning (RL) can learn control policies from static datasets but, like
standard RL methods, it requires reward annotations for every transition. In many cases …

This paper studies offline Imitation Learning (IL) where an agent learns to imitate an expert
demonstrator without additional online environment interactions. Instead, the learner is …