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