Related papers: Reinforcement Learning for Mixed Autonomy Intersections

URL: http://arxiv.org/abs/2111.04686v1
Date: Mon, 8 Nov 2021 18:03:18 GMT
Title: Reinforcement Learning for Mixed Autonomy Intersections
Authors: Zhongxia Yan, Cathy Wu
Abstract summary: We propose a model-free reinforcement learning method for controlling mixed autonomy traffic in simulated traffic networks. Our method utilizes multi-agent policy decomposition which allows decentralized control based on local observations for an arbitrary number of controlled vehicles.
Score: 4.771833920251869
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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