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SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

Authors :
Zhou, Ming
Luo, Jun
Villella, Julian
Yang, Yaodong
Rusu, David
Miao, Jiayu
Zhang, Weinan
Alban, Montgomery
Fadakar, Iman
Chen, Zheng
Huang, Aurora Chongxi
Wen, Ying
Hassanzadeh, Kimia
Graves, Daniel
Chen, Dong
Zhu, Zhengbang
Nguyen, Nhat
Elsayed, Mohamed
Shao, Kun
Ahilan, Sanjeevan
Zhang, Baokuan
Wu, Jiannan
Fu, Zhengang
Rezaee, Kasra
Yadmellat, Peyman
Rohani, Mohsen
Nieves, Nicolas Perez
Ni, Yihan
Banijamali, Seyedershad
Rivers, Alexander Cowen
Tian, Zheng
Palenicek, Daniel
Ammar, Haitham bou
Zhang, Hongbo
Liu, Wulong
Hao, Jianye
Wang, Jun
Publication Year :
2020

Abstract

Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently interact with diverse road users in diverse scenarios remains largely unsolved. Learning methods have much to offer towards solving this problem. But they require a realistic multi-agent simulator that generates diverse and competent driving interactions. To meet this need, we develop a dedicated simulation platform called SMARTS (Scalable Multi-Agent RL Training School). SMARTS supports the training, accumulation, and use of diverse behavior models of road users. These are in turn used to create increasingly more realistic and diverse interactions that enable deeper and broader research on multi-agent interaction. In this paper, we describe the design goals of SMARTS, explain its basic architecture and its key features, and illustrate its use through concrete multi-agent experiments on interactive scenarios. We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving. Our code is available at https://github.com/huawei-noah/SMARTS.<br />Comment: 20 pages, 11 figures. Paper accepted to CoRL 2020

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2010.09776
Document Type :
Working Paper