
This research demonstrates the feasibility of using a decentralized multi-agent reinforcement learning approach to control mixed traffic (human-driven and robot vehicles) at complex, unsignalized intersections, leading to significant improvements in traffic efficiency and congestion reduction.


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decentralized-reinforcement-learning-for-mixed-traffic-control-at-complex-unsignalized-intersections-using-robot-vehicles


Decentralized Reinforcement Learning for Mixed Traffic Control at Complex, Unsignalized Intersections Using Robot Vehicles


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混合交通の安全性、安定性、効率を向上させるためにリアルワールドの乱れを強化するEnduRLプロジェクト。


endurl-enhancing-safety-stability-and-efficiency-of-mixed-traffic-under-real-world-perturbations-via-reinforcement-learning


EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning



Reinforcement learning improves safety, efficiency, and stability in mixed traffic.
