论文标题
影响力的本地模拟器：大型网络系统中快速深度RL的可扩展解决方案
Influence-Augmented Local Simulators: A Scalable Solution for Fast Deep RL in Large Networked Systems
论文作者
论文摘要
对现实世界问题的学习有效政策仍然是强化学习领域（RL）的开放挑战。主要限制是所需的数据量以及获得该数据的步伐。在本文中，我们研究了如何构建复杂系统的轻量级模拟器，这些模拟器可以足够快地运行，以使深度RL适用。我们专注于代理与较大环境的减少部分相互作用的域，同时仍受到全球动态的影响。我们的方法结合了局部模拟器的使用与模仿全球系统影响的学习模型。实验表明，将此想法纳入深度RL工作流程可以大大加速培训过程，并为未来提供一些机会。
Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for deep RL to be applicable. We focus on domains where agents interact with a reduced portion of a larger environment while still being affected by the global dynamics. Our method combines the use of local simulators with learned models that mimic the influence of the global system. The experiments reveal that incorporating this idea into the deep RL workflow can considerably accelerate the training process and presents several opportunities for the future.
