论文标题
寻找排队模型的最佳策略：新参数化
Finding Optimal Policy for Queueing Models: New Parameterization
论文作者
论文摘要
排队系统出现在许多重要的现实生活应用中，包括通信网络，运输和制造系统。强化学习（RL）框架是排队控制问题的合适模型，在该问题中，基础动态通常未知，并且代理从环境中收到很少的信息来导航。在这项工作中，我们将排队模型的优化方面作为RL环境，并提供了有效学习最佳政策的见解。我们通过使用排队网络系统的固有属性来提出对策略的新参数化。实验显示了我们的方法的良好性能，从光到交通繁忙的各种负载条件。
Queueing systems appear in many important real-life applications including communication networks, transportation and manufacturing systems. Reinforcement learning (RL) framework is a suitable model for the queueing control problem where the underlying dynamics are usually unknown and the agent receives little information from the environment to navigate. In this work, we investigate the optimization aspects of the queueing model as a RL environment and provide insight to learn the optimal policy efficiently. We propose a new parameterization of the policy by using the intrinsic properties of queueing network systems. Experiments show good performance of our methods with various load conditions from light to heavy traffic.
