Reinforcement learning in multi-agent environment and ant colony for packet scheduling in routers
Proceedings of the 5th ACM international workshop on Mobility management and wireless access
Reinforcing probabilistic selective Quality of Service routes in dynamic irregular networks
Computer Communications
Design and performance analysis of an inductive QoS routing algorithm
Computer Communications
QoS swarm state dependent routing for irregular traffic in telecommunication networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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In this paper, we propose two adaptive routing algorithms based on reinforcement learning. In the first algorithm, we have used a neural network to approximate the reinforcement signal, allowing the learner to take into account various parameters such as local queue size, for distance estimation. Moreover, each router uses an online learning module to optimize the path in terms of average packet delivery time, by taking into account the waiting queue states of neighbouring routers. In the second algorithm, the exploration of paths is limited to N-best non-loop paths in terms of hops number (number of routers in a path), leading to a substantial reduction of convergence time. The performances of the proposed algorithms are evaluated experimentally with OPNET simulator for different levels of traffic's load and compared with standard shortest-path and Q-routing algorithms. Our approach proves superior to classical algorithms and is able to route efficiently even when the network load varies in an irregular manner. We also tested our approach on a large network topology to proof its scalability and adaptability. Copyright © 2006 John Wiley & Sons, Ltd.