Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning
This provides a tool for MBRL researchers to facilitate experiments, but it is incremental as it builds on existing methods without introducing new algorithmic advances.
The authors tackled the lack of reusable open-source frameworks for Model-Based Reinforcement Learning (MBRL) by developing Baconian, a flexible and modularized framework that allows researchers to easily implement MBRL testbeds, saving effort on experiments.
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.