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Reinforcement Learning Smooths Highway Traffic, Boosting Efficiency

A recent study showcased the power of reinforcement learning in managing real-world traffic congestion. By deploying 100 RL-controlled cars during rush hour, researchers were able to smooth traffic flow, reduce frustrating speed fluctuations, and improve overall fuel efficiency. The autonomous vehicles, equipped with controllers deployable on most modern vehicles, learned to optimize energy use and maintain safety through data-driven simulations. The results indicated that even a small number of well-controlled autonomous vehicles can lead to significant improvements in traffic and fuel economy for all drivers.

calendar_today 2025-03-25 attribution bair.berkeley.edu/blog/

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

Researchers deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption. They tackled frustrating slowdowns and speedups by training efficient flow-smoothing controllers using fast, data-driven simulations that RL agents interact with, learning to maximize energy efficiency while maintaining throughput and operating safely around human drivers. The experiment demonstrated that a small proportion of well-controlled autonomous vehicles (AVs) can significantly improve traffic flow and fuel efficiency for all drivers, using controllers deployable on most modern vehicles with standard radar sensors, operating in a decentralized manner.
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