Multi-agent learning for control of Internet traffic routing

This paper discusses the use of multi-agent learning to control routing within an Internet. The agents are distributed throughout the network. They use reinforcement learning to adapt their behaviour to network conditions. These agents are based on Watkin's Q-learning algorithm (1992). Simulation results are provided to show the contribution of multi-agent learning to both network reliability and efficiency.