  Model-based reinforcement learning is a compelling framework for
data-efficient learning of agents that interact with the world. This family of
algorithms has many subcomponents that need to be carefully selected and tuned.
As a result the entry-bar for researchers to approach the field and to deploy
it in real-world tasks can be daunting. In this paper, we present MBRL-Lib -- a
machine learning library for model-based reinforcement learning in continuous
state-action spaces based on PyTorch. MBRL-Lib is designed as a platform for
both researchers, to easily develop, debug and compare new algorithms, and
non-expert user, to lower the entry-bar of deploying state-of-the-art
algorithms. MBRL-Lib is open-source at
https://github.com/facebookresearch/mbrl-lib.
