
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。


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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


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Proposing a novel multi-agent actor-critic framework with interpretable influence mechanism and biased ReLU approximation for efficient traffic signal control.


multi-agent-reinforcement-traffic-signal-control-with-interpretable-influence-mechanism-and-biased-relu-approximation


Multi-agent Reinforcement Traffic Signal Control with Interpretable Influence Mechanism and Biased ReLU Approximation



Car-following models encompass various disciplines and provide insights into traffic flow dynamics, aiding in the development of connected and automated transportation systems.


Car-Following-Models-A-Comprehensive-Review


Car-Following Models: A Comprehensive Review
