Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Link-level measurements from an 802.11b mesh network
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Routing in multi-radio, multi-hop wireless mesh networks
Proceedings of the 10th annual international conference on Mobile computing and networking
ExOR: opportunistic multi-hop routing for wireless networks
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Asymptotic Capacity of Infrastructure Wireless Mesh Networks
IEEE Transactions on Mobile Computing
Joint Throughput Optimization for Wireless Mesh Networks
IEEE Transactions on Mobile Computing
Augmenting predictive with oblivious routing for wireless mesh networks under traffic uncertainty
Computer Networks: The International Journal of Computer and Telecommunications Networking
Survey on diversity-based routing in wireless mesh networks: Challenges and solutions
Computer Communications
Mesh networks: commodity multihop ad hoc networks
IEEE Communications Magazine
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Classical routing protocols for WMNs are typically designed to achieve specific target objectives (e.g., maximum throughput), and they offer very limited flexibility. As a consequence, more intelligent and adaptive mesh networking solutions are needed to obtain high performance in diverse network conditions. To this end, we propose a reinforcement learning-based routing framework that allows each mesh device to dynamically select at run time a routing protocol from a pre-defined set of routing options, which provides the best performance. The most salient advantages of our solution are: i) it can maximize routing performance considering different optimization goals, ii) it relies on a compact representation of the network state and it does not need any model of its evolution, and iii) it efficiently applies Q-learning methods to guarantee convergence of the routing decision process. Through extensive ns-2 simulations we show the superior performance of the proposed routing approach in comparison with two alternative routing schemes.