  Model-Based Reinforcement Learning (MBRL) is one category of Reinforcement
Learning (RL) algorithms which can improve sampling efficiency by modeling and
approximating system dynamics. It has been widely adopted in the research of
robotics, autonomous driving, etc. Despite its popularity, there still lacks
some sophisticated and reusable open-source frameworks to facilitate MBRL
research and experiments. To fill this gap, we develop a flexible and
modularized framework, Baconian, which allows researchers to easily implement a
MBRL testbed by customizing or building upon our provided modules and
algorithms. Our framework can free users from re-implementing popular MBRL
algorithms from scratch thus greatly save users' efforts on MBRL experiments.
