arXiv:2501. 17559v2 Announce Type: replace Abstract: After the achievement of solving two-player zero-sum games, more AI researchers focus on solving multiplayer games.
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JointMatch is a learning-based framework that simultaneously handles request pairing and vehicle assignment for ride‑sharing using a single, sparsified heterogeneous graph neural network. By scoring all candidate decisions in one forward pass, it scales linearly with the number of vehicles and requests, outperforming classical heuristics and two‑stage GNN baselines on New York City Yellow Taxi data. The model achieves significant speedups—over 20× faster per dispatch epoch at city scale—and further improves revenue through supervised training and policy‑gradient fine‑tuning.
arXiv:2609.16163v1 Announce Type: new Abstract: The sharp increase in mass shootings underscores an urgent need for systems that guide victims to safety in real time. An effective evacuation system m...
The paper introduces a scalable heterogeneous Graph Neural Network framework that automatically generates and evaluates shared roadmaps for multi‑agent path planning in continuous environments. By representing waypoints, agent locations, and task locations as distinct nodes, the GNN learns from expert solver trajectories to identify critical points and prune redundant connections, producing a compact, coordination‑aware roadmap. Experiments show the approach can reduce planning effort, achieving at least a 40% reduction in runtime and graph size for dense roadmaps.
The paper presents a graph-based framework for large-scale railway network management, combining a hierarchical Bayesian model with a Gaussian Process on a graph kernel to infer spatially correlated maintenance environments from Swiss Federal Railways data. It introduces a topology-aware Multi-Agent Reinforcement Learning system that uses graph neural networks and Transformers to optimize network-level policies. The approach demonstrates scalability via zero-shot transfer learning, enabling agents trained on small network segments to perform effectively on unseen large networks, outperforming heuristics and standard MARL baselines while reducing training time.
Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation methods, such as lattice grids or...
arXiv:2408.15538v4 Announce Type: replace Abstract: While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with...