AI RESEARCH
RouteFormer: A Transformer-Based Routing Framework for Autonomous Vehicles
arXiv CS.AI
•
ArXi:2504.05407v2 Announce Type: replace-cross Autonomous surveillance missions in Internet of Things (IoT) networks often involve solving NP-hard combinatorial optimization problems to ensure efficient resource utilization. To address the limitations of conventional heuristics in dynamic environments, we propose RouteFormer, a novel framework for single-agent routing in graph-based terrains. RouteFormer creates a synergy between the global context awareness of the transformer self-attention mechanism and the adaptive decision-making capabilities of Reinforcement Learning (RL.