MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

• URL: http://arxiv.org/abs/2104.10159v1
• Date: Tue, 20 Apr 2021 17:58:22 GMT
• Title: MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
• Authors: Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra
• Abstract summary: We present MBRL-Lib -- a machine learning library for model-based reinforcement learning in continuous state-action spaces based on PyTorch. It 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.
• Score: 13.467075854633213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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