MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
This library addresses the problem for researchers and non-expert users by providing a tool to simplify algorithm development and application, though it is incremental as it builds on existing methods without introducing new algorithms.
The authors tackled the complexity and high entry barrier in model-based reinforcement learning by developing MBRL-Lib, a modular library based on PyTorch for continuous state-action spaces, which is open-sourced to facilitate research and deployment.
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.