With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

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 methods for reinforcement learning have a potential to help bridge the gap between
reinforcement learning research and real-world applications. They make it possible to learn …

Offline methods for reinforcement learning have a potential to help bridge the gap between
reinforcement learning research and real-world applications. They make it possible to learn …

Recent progress in deep learning has relied on access to large and diverse datasets. Such
data-driven progress has been less evident in offline reinforcement learning (RL), because …

The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy
is learned from a static dataset, is compelling as progress enables RL methods to take …

Reinforcement learning (RL) provides an appealing formalism for learning control policies
from experience. However, the classic active formulation of RL necessitates a lengthy active …

In reinforcement learning (RL) research, it is common to assume access to direct online
interactions with the environment. However in many real-world applications, access to the …

Offline reinforcement learning (RL) aims at learning effective policies from historical data
without extra environment interactions. During our experience of applying offline RL, we …

Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …