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