Title: Benchmarking Model-Based Reinforcement Learning. Abstract: Model-based reinforcement learning (MBRL) is widely seen as having the potential
to be significantly more sample efficient than model-free RL. However, research in
model-based RL has not been very standardized. It is fairly common for authors to
experiment with self-designed environments, and there are several separate lines of
research, which are sometimes closed-sourced or not reproducible. Accordingly, it
is an open question how these various existing algorithms perform relative to each
other. To facilitate research in MBRL, in this paper we gather a wide collection
of MBRL algorithms and propose over 18 benchmarking environments specially
designed for MBRL. We benchmark these algorithms with unified problem settings,
including noisy environments. Beyond cataloguing performance, we explore
and unify the underlying algorithmic differences across MBRL algorithms. We
characterize three key research challenges for future MBRL research: the dynamics
bottleneck, the planning horizon dilemma, and the early-termination dilemma.
Finally, to facilitate future research on MBRL, we open-source our benchmark.