Reinforcement Learning for Mixed Autonomy Intersections
Name
2111.04686.pdf
Description
Accepted version
Size
2.08 MB
Format
Adobe PDF
Checksum (MD5)
c7124eb100cb392971473f3f5c8a254b
Author(s)
Yan, Zhongxia
Wu, Cathy
Date Issued
2021
Journal
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
Publisher
IEEE
Citation
Yan, Zhongxia and Wu, Cathy. 2021. "Reinforcement Learning for Mixed Autonomy Intersections." 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).
Version
Author's final manuscript
Abstract
We propose a model-free reinforcement learning method for controlling mixed
autonomy traffic in simulated traffic networks with through-traffic-only
two-way and four-way intersections. Our method utilizes multi-agent policy
decomposition which allows decentralized control based on local observations
for an arbitrary number of controlled vehicles. We demonstrate that, even
without reward shaping, reinforcement learning learns to coordinate the
vehicles to exhibit traffic signal-like behaviors, achieving near-optimal
throughput with 33-50% controlled vehicles. With the help of multi-task
learning and transfer learning, we show that this behavior generalizes across
inflow rates and size of the traffic network. Our code, models, and videos of
results are available at
https://github.com/ZhongxiaYan/mixed_autonomy_intersections.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ITSC48978.2021.9565000
