Technical Note: \cal Q-Learning
Machine Learning
Ant algorithms for discrete optimization
Artificial Life
The use of learning algorithms in ATM networks call admission control problem: a methodology
Computer Networks: The International Journal of Computer and Telecommunications Networking
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Ad-hoc On-Demand Distance Vector Routing
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A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Performance evaluation of routing protocols for ad hoc wireless networks
Mobile Networks and Applications
Routing in multi-radio, multi-hop wireless mesh networks
Proceedings of the 10th annual international conference on Mobile computing and networking
A high-throughput path metric for multi-hop wireless routing
Wireless Networks - Special issue: Selected papers from ACM MobiCom 2003
A Routing Metric for Load-Balancing in Wireless Mesh Networks
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Context-based routing: techniques, applications and experience
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
Novel reinforcement learning-based approaches to reduce loss probability in buffer-less OBS networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Routing Metric for Interference and Channel Diversity in Multi-Radio Wireless Mesh Networks
ADHOC-NOW '09 Proceedings of the 8th International Conference on Ad-Hoc, Mobile and Wireless Networks
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
Wireless mesh networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cognitive network management with reinforcement learning for wireless mesh networks
IPOM'07 Proceedings of the 7th IEEE international conference on IP operations and management
Reinforcement learning-based best path to best gateway scheme for wireless mesh networks
WIMOB '11 Proceedings of the 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications
Design of scalable and efficient multi-radio wireless networks
Wireless Networks
The nominal capacity of wireless mesh networks
IEEE Wireless Communications
Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Routing Metrics and Protocols for Wireless Mesh Networks
IEEE Network: The Magazine of Global Internetworking
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This paper addresses the problem of efficient routing in backbone wireless mesh networks (WMNs) where each mesh router is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet. Most routing schemes have been designed to reduce routing costs by optimizing one metric, e.g., hop count and interference ratio. However, when considering these metrics together, the complexity of the routing problem increases drastically. Thus, an efficient and adaptive routing scheme that takes into account several metrics simultaneously and considers traffic congestion around the gateways is needed. In this paper, we propose an adaptive scheme for routing traffic in WMNs, called Reinforcement Learning-based Distributed Routing (RLBDR), that (1) considers the critical areas around the gateways where mesh routers are much more likely to become congested and (2) adaptively learns an optimal routing policy taking into account multiple metrics, such as loss ratio, interference ratio, load at the gateways and end-to end delay. Simulation results show that RLBDR can significantly improve the overall network performance compared to schemes using either Metric of Interference and Channel switching, Best Path to Best Gateway, Expected Transmission count, nearest gateway (i.e., shortest path to gateway) or load at gateways as a metric for path selection.