Abstract:
Efficient routing of information packets in dynamically changing communication networks requires routing policies that adapt to changes in load levels, traffic patterns and network topologies. Reinforcement Learning (RL) is an area of artificial intelligence that studies algorithms that dynamically optimize their performance based on experience in an environment. RL, thus, is a promising framework for developing adaptive network routing algorithms and there have been a number of proposed RL-based routing algorithms. In this project, we developed an infrastructure for evaluating RL-based routing mechanisms and use it to evaluate and compare a number of existing algorithms under various network conditions.
