Scalable multiagent driving policies for reducing traffic congestion

Jiaxun Cui, William Macke, Harel Yedidsion, Aastha Goyal, Daniel Urieli, Peter Stone

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small scale mixed traffic scenarios with both AVs and human-driven vehicles, a small fraction of AVs executing a controlled multiagent driving policy can mitigate congestion. In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We start by showing that a congestion metric used by past research is manipulable in open road network scenarios where vehicles dynamically join and leave the road. We then propose using a different metric that is robust to manipulation and reflects open network traffic efficiency. Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles). Additionally, our modular transfer learning approach saves up to 80% of the training time in our experiments, by focusing its data collection on key locations in the network. Finally, we show for the first time a distributed multiagent policy that improves congestion over human-driven traffic. The distributed approach is more realistic and practical, as it relies solely on existing sensing and actuation capabilities, and does not require adding new communication infrastructure.

Original languageEnglish
Title of host publication20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages386-394
Number of pages9
ISBN (Electronic)9781713832621
StatePublished - 1 Jan 2021
Externally publishedYes
Event20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021 - Virtual, Online
Duration: 3 May 20217 May 2021

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume1
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
CityVirtual, Online
Period3/05/217/05/21

Keywords

  • Autonomous vehicles
  • Deep reinforcement learning
  • Flow
  • Multiagent reinforcement learning
  • Multiagent systems
  • Traffic optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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