2002/07/21 by Srinidhi Varadarajan, Naren Ramakrishnan, Varadarajan, Srinidhi +1
Computer Science · #Artificial Intelligence (cs.AI) #C.2.2 #FOS: Computer and information sciences #I.2.6 #Networking and Internet Architecture (cs.NI) #Optimization and Search Problems #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #cs.AI #cs.NI
paper · pdf · doi:10.48550/arxiv.cs/0207073
arxiv created 2002/07/21 · openalex publication_date 2002/07/21 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper studies the evaluation of routing algorithms from the perspective of reachability routing, where the goal is to determine all paths between a sender and a receiver. Reachability routing is becoming relevant with the changing dynamics of the Internet and the emergence of low-bandwidth wireless/ad-hoc networks. We make the case for reinforcement learning as the framework of choice to realize reachability routing, within the confines of the current Internet infrastructure. The setting of the reinforcement learning problem offers several advantages, including loop resolution, multi-path forwarding capability, cost-sensitive routing, and minimizing state overhead, while maintaining the incremental spirit of current backbone routing algorithms. We identify research issues in reinforcement learning applied to the reachability routing problem to achieve a fluid and robust backbone routing framework. The paper is targeted toward practitioners seeking to implement a reachability routing algorithm.