基于多智能体深度强化学习的智能电网光网络切片方案

Smart Grid Optical Network Slicing Scheme Based on Multi-agent Deep Reinforcement Learning

摘要:

为了提高光网络对大规模、差异化电力业务的资源分配能力,降低大规模业务的算法训练时间,提出了一种基于多智能体深度确定性策略梯度算法的智能电网光网络资源分配方案。该方案考虑大规模和差异化电力业务,将智能电网光网络建模成多智能体系统,以最大化电网公司收益为目标,建立了智能电网光核心网络切片模型,进行网络资源分配优化,并采用条件判断映射,简化了优化问题。同时,把不同业务部署到不同智能体中进行运算,以降低训练时间,满足网络实时性需求。仿真结果表明,该算法具有更大的奖励、更低的成本、时延和训练时间。

Abstract:

In order to improve the resource allocation ability of optical networks for massive and differentiated power services and reduce the algorithm training time of large-scale services, a smart grid optical network resource allocation scheme based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm was proposed. The large-scale and differentiated power services were considered, the optical core network slice model of smart grid was built and the optimization problem aiming at maximizing the income of power grid companies was proposed. Conditional judgment mapping was proposed to simplify the optimization problem. At the same time, the improved MADDPG algorithm was designed to reduce the training time and meet the real-time needs of the network by placing different services to different agents. Lastly, simulation results show that the proposed algorithm has better reward, lower cost and time delay, and lesser training time.