  Model-based approaches are becoming increasingly popular in the field of
offline reinforcement learning, with high potential in real-world applications
due to the model's capability of thoroughly utilizing the large historical
datasets available with supervised learning techniques. This paper presents a
literature review of recent work in offline model-based reinforcement learning,
a field that utilizes model-based approaches in offline reinforcement learning.
The survey provides a brief overview of the concepts and recent developments in
both offline reinforcement learning and model-based reinforcement learning, and
discuss the intersection of the two fields. We then presents key relevant
papers in the field of offline model-based reinforcement learning and discuss
their methods, particularly their approaches in solving the issue of
distributional shift, the main problem faced by all current offline model-based
reinforcement learning methods. We further discuss key challenges faced by the
field, and suggest possible directions for future work.
