Abstract
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 algorithms that utilize previously collected
data, without additional online data collection. Offline reinforcement learning
algorithms hold tremendous promise for making it possible to turn large
datasets into powerful decision making engines. Effective offline reinforcement
learning methods would be able to extract policies with the maximum possible
utility out of the available data, thereby allowing automation of a wide range
of decision-making domains, from healthcare and education to robotics. However,
the limitations of current algorithms make this difficult. We will aim to
provide the reader with an understanding of these challenges, particularly in
the context of modern deep reinforcement learning methods, and describe some
potential solutions that have been explored in recent work to mitigate these
challenges, along with recent applications, and a discussion of perspectives on
open problems in the field.
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