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 …

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 …

The personalized titration and optimization of insulin regimens for treatment of type 2
diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based …

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 …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

In many real-world reinforcement learning applications, access to the environment is limited
to a fixed dataset, instead of direct (online) interaction with the environment. When using this …

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-
policy data, especially if they cannot explore online in the environment. These are critical …

When learning policies for real-world domains, two important questions arise:(i) how to
efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate …

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 …

In recommender systems, usually the ratings of a user to most items are missing and a
critical problem is that the missing ratings are often missing not at random (MNAR) in reality …