SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban
Abstract
Interaction is fundamental in autonomous driving (AD). Despite more than a decade of intensive R&D in AD, how to dynamically interact with diverse road users in various contexts still remains unsolved. Multi-agent learning has recently seen big breakthroughs and has much to offer towards solving realistic interaction in AD. However, to realize this potential we need multi-agent AD simulation of realistic interaction. To break this apparent chicken-and-egg circularity, we built an AD simulation platform called SMARTS (Scalable Multi-Agent Rl Training School), which is designed to accumulate behavior models of road users towards increasingly realistic and diverse interaction that in turn enables deeper and broader multi-agent research on interaction. In this paper, we describe the design goals of SMARTS, explain its key architectural ideas, illustrate its use for multi-agent research through experiments on concrete interaction scenarios, and introduce a set of benchmarks and metrics. As an open-source, industrial-strength platform, the future of SMARTS lies in its growth along with the multi-agent research it enables in the years to come.
BibTeX
@inproceedings{corl2020_smartsanopensour,
title = {SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving},
author = {Ming Zhou and Jun Luo and Julian Villella and Yaodong Yang and David Rusu and Jiayu Miao and Weinan Zhang and Montgomery Alban and IMAN FADAKAR and Zheng Chen and Chongxi Huang and Ying Wen and Kimia Hassanzadeh and Daniel Graves and Zhengbang Zhu and Yihan Ni and Nhat Nguyen and Mohamed Elsayed and Haitham Ammar and Alexander Cowen-Rivers and Sanjeevan Ahilan and Zheng Tian and Daniel Palenicek and Kasra Rezaee and Peyman Yadmellat and Kun Shao and dong chen and Baokuan Zhang and Hongbo Zhang and Jianye Hao and Wulong Liu and Jun Wang},
booktitle = {CoRL 2020},
year = {2020}
}