HetGPS: Scalable Graph Multi-Agent Reinforcement Learning with Physics-Anchored
arXiv:2508.20784v2 Announce Type: replace Abstract: Bus bunching remains a challenge for urban transit due to stochastic traffic and passenger demand. Traditional solutions rely
arXiv:2606.31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as
arXiv:2604.14032v1 Announce Type: new Abstract: Reinforcement learning has shown promise for automating power-grid operation tasks such as topology control and congestion
arXiv:2604.27849v1 Announce Type: new Abstract: This paper presents a configurable, grid-aware Agent-Based Model (ABM) for the systematic analysis of electric vehicle (EV)
arXiv:2606.27577v1 Announce Type: cross Abstract: This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where
arXiv:2604.05254v1 Announce Type: new Abstract: Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane -- from order
arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment
arXiv:2603.03686v2 Announce Type: replace Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional
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