Generative Adversarial Network-Based Transfer Reinforcement Learning for Routing With Prior Knowledge

With the incremental deployment of software defined networking, the routing algorithms have gained more power on observability and controllability. Deep reinforcement learning, as an experience-driven approach, shows considerable potential in routing problem with the help of the centralized controller. It is an adaptive, lightweight, and model-free approach to coping with dynamic runtime status, large-scale traffic, and heterogeneous objective of SDN routing. However, it is still not suitable for the variable and complex emerging networks, because the huge training cost prevents fast convergence in a varying or discrepant environment. In this paper, we propose a transfer reinforcement learning algorithm to improve the training efficiency, and handle the variation in network status and topology. Specifically, we leverage the generative adversarial network to learn domain-invariant features that is suitable for deep reinforcement learning-based routing in different network environments. This mechanism utilizes the previous model and accelerates the training process. We implement our routing algorithm in the production level software switches and controller, while evaluating it comprehensively with many topologies and network status distributions. The experimental results show that our work not only outperforms the state-of-the-art deep reinforcement learning-based routing frameworks, but also has more training efficiency than the naive transfer learning algorithm both on different topologies and network status distributions.