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аналитика - Mixed Traffic Control
Decentralized Reinforcement Learning for Mixed Traffic Control at Complex, Unsignalized Intersections Using Robot Vehicles
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.
EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning
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
Reinforcement learning improves safety, efficiency, and stability in mixed traffic.
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