In most modern cities, traffic congestion is one of the most salient societal challenges. Past research has shown that inserting a limited number of autonomous vehicles (AVs) within the traffic flow, with driving policies learned specifically for the purpose of reducing congestion, can significantly improve traffic conditions. However, to date these AV policies have generally been evaluated under the same limited conditions under which they were trained. On the other hand, to be considered for practical deployment, they must be robust to a wide variety of traffic conditions. This paper establishes for the first time that a multiagent driving policy can be trained in such a way that it generalizes to different traffic flows, AV penetration, and road geometries, including on multi-lane roads.

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In Proceedings of the Adaptive and Learning Agents Workshop (ALA), Auckland, NZ, May 2022.

@inproceedings{ALA22-zhang, title={Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction}, author={Yulin Zhang and William Macke and Jiaxun Cui and Daniel Urieli and Peter Stone}, booktitle={Proceedings of the Adaptive and Learning Agents Workshop (ALA)}, month={May}, address={Auckland, NZ}, url="http://nn.cs.utexas.edu/?ALA22-zhang", year={2022} }