Data networks
Routing in communications networks
Routing in communications networks
Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Ethereal: a fault tolerant host-transparent mechanism for bandwidth guarantees over switched ethernet networks
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Ants and reinforcement learning: a case study in routing in dynamic networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
STEWARD: demo of spatio-textual extraction on the web aiding the retrieval of documents
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
Reinforcing probabilistic selective Quality of Service routes in dynamic irregular networks
Computer Communications
Traffic aware medium access control protocol for wireless sensor networks
Proceedings of the 7th ACM international symposium on Mobility management and wireless access
Status-based routing in baggage handling systems: searching verses learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
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. This paper also presents the design, implementation and evaluation of a new reachability routing algorithm that uses a model-based approach to achieve cost-sensitive multi-path forwarding; performance assessment of the algorithm in various troublesome topologies shows consistently superior performance over classical reinforcement learning algorithms. The paper is targeted toward practitioners seeking to implement a reachability routing algorithm.