Selflearning Routing for Optical Networks It is generally very difficult to optimize the routing policies in optical networks with dynamic traffic. Most widelyused routing policies, e.g., shortest path routing and least congested path (LCP) routing, are heuristic policies. Although the LCP is often regarded as the bestperforming adaptive routing policy, we are often eager to know whether there exist better routing policies that surpass these heuristics in performance. In this paper, we propose a framework of reinforcement learning (RL) based routing scheme, that learns routing decisions during the interactions with the environment. With a proposed selflearning method, the RL agent can improve its routing policy continuously. Simulations on a ringtopology metro optical network demonstrate that, the proposed scheme outperforms the LCP routing policy.

Reinforcement LearningDriven QoSAware Intelligent Routing for SoftwareDefined Networks Softwaredefined network (SDN) is an emerging computer networking technology that disjoints the data forwarding from the centralized control and enables a highly manageable and flexible networking paradigm. There has been intensive research developed for efficient routing and resource allocation for SDNs. However, there still remain essential challenges to achieve situationawareness networking management to ensure the applicationdriven QualityofService (QoS) even in the presence of cyber attacks. To address this issue, in this paper, we exploit reinforcement learning (RL) technologies to develop a situationawareness and intelligent networking management from the perspective of routing management. The performance of our proposed RLenabled routing management method is evaluated in the simulation sections by considering various scenarios.