Software Defined Network is an emerging paradigm in computer networks. The separation of the control plane from the forwarding plane in this arrangement has different aspects. This splitting provides many advantages like easy manageability and configuration. Along with benefits, various issues specific to this paradigm also arise. Routing management in such a paradigm deals with diverse concerns, objectives, and parameters before selecting the best route. Reinforcement Learning has already proven its strength in distinct fields like business, industry automation, gaming, algorithms, etc. Even routing in a network can also be made efficient using concepts defined in reinforcement learning. In this paper, routing within a controller's area is modeled, keeping scalability in mind; and an optimal solution is provided using learning. Both proactive and reactive approaches are used for flow installation, and the link load is utilized optimally. The area under a particular controller is efficiently routed, and it tweaks the network. Q-learning model helps to learn the optimal path and provide the best route in case of a failure. Once the learning completes, the model works on it. Preliminary evaluation depicts that improvement of 78%, 58%, and 47 % is achieved for the number of messages generation when compared with other already exiting solutions for routing in Software Defined Networks.

软件定义网络是计算机网络中的一种新兴范式。在这种安排中，控制平面与转发平面的分离具有不同的方面。这种拆分提供了许多优点，例如易于管理和配置。除了好处之外，还出现了特定于这种范式的各种问题。在选择最佳路由之前，这种范式中的路由管理处理不同的关注点、目标和参数。强化学习已经在商业、工业自动化、游戏、算法等不同领域证明了自己的实力。即使是网络中的路由也可以使用强化学习中定义的概念来提高效率。在本文中，对控制器区域内的路由进行建模，同时牢记可扩展性；并使用学习提供最佳解决方案。主动和被动方法都用于流量安装，并且链路负载得到最佳利用。特定控制器下的区域被有效路由，并调整网络。Q-learning 模型有助于学习最佳路径并在发生故障时提供最佳路线。一旦学习完成，模型就会对其进行处理。初步评估表明，与其他已经存在的软件定义网络路由解决方案相比，消息生成数量提高了 78%、58% 和 47%。一旦学习完成，模型就会对其进行处理。初步评估表明，与其他已经存在的软件定义网络路由解决方案相比，消息生成数量提高了 78%、58% 和 47%。一旦学习完成，模型就会对其进行处理。初步评估表明，与其他已经存在的软件定义网络路由解决方案相比，消息生成数量提高了 78%、58% 和 47%。