  Deep reinforcement learning has shown remarkable success in the past few
years. Highly complex sequential decision making problems from game playing and
robotics have been solved with deep model-free methods. Unfortunately, the
sample complexity of model-free methods is often high. To reduce the number of
environment samples, model-based reinforcement learning creates an explicit
model of the environment dynamics. Achieving high model accuracy is a challenge
in high-dimensional problems. In recent years, a diverse landscape of
model-based methods has been introduced to improve model accuracy, using
methods such as uncertainty modeling, model-predictive control, latent models,
and end-to-end learning and planning. Some of these methods succeed in
achieving high accuracy at low sample complexity, most do so either in a
robotics or in a games context. In this paper, we survey these methods; we
explain in detail how they work and what their strengths and weaknesses are. We
conclude with a research agenda for future work to make the methods more robust
and more widely applicable to other applications.
