  Deep reinforcement learning has shown remarkable success in the past few
years. Highly complex sequential decision making problems have been solved in
tasks such as game playing and robotics. Unfortunately, the sample complexity
of most deep reinforcement learning methods is high, precluding their use in
some important applications. Model-based reinforcement learning creates an
explicit model of the environment dynamics to reduce the need for environment
samples. Current deep learning methods use high-capacity networks to solve
high-dimensional problems. Unfortunately, high-capacity models typically
require many samples, negating the potential benefit of lower sample complexity
in model-based methods. A challenge for deep model-based methods is therefore
to achieve high predictive power while maintaining low sample complexity. In
recent years, many model-based methods have been introduced to address this
challenge. In this paper, we survey the contemporary model-based landscape.
First we discuss definitions and relations to other fields. We propose a
taxonomy based on three approaches: using explicit planning on given
transitions, using explicit planning on learned transitions, and end-to-end
learning of both planning and transitions. We use these approaches to organize
a comprehensive overview of important recent developments such as latent
models. We describe methods and benchmarks, and we suggest directions for
future work for each of the approaches. Among promising research directions are
curriculum learning, uncertainty modeling, and use of latent models for
transfer learning.
