论文标题
复杂网络物理系统的基于AI的建模，探索和操作的对抗性弹性学习体系结构
The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems
论文作者
论文摘要
深钢筋学习领域（DRL）的现代算法表现出了杰出的成功。最广为人知的是那些在基于游戏的方案中，从Atari视频游戏和Starcraft〜\ textsc {ii}实时策略游戏。但是，利用各种DRL算法的现代网络物理系统（CPS）领域的应用很少。我们假设好处将是可观的：现代CP已变得越来越复杂，并且超出了传统的建模和分析方法。同时，这些CP面临着越来越多的随机输入，从电网的挥发性能源到来自市场的广泛用户参与。使用人工智能领域（AI）的技术建模的方法不关注分析和操作。在本文中，我们描述了对抗性弹性学习（ARL）的概念，该概念为复杂的环境检查和弹性操作制定了新的方法：它定义了两个代理类，攻击者和后卫代理。 ARL的精髓在于探索系统和相互训练的代理，而无需任何领域知识。在这里，我们介绍了ARL软件体系结构，该体系结构允许使用广泛的无模型和基于模型的基于DRL的算法，并且混凝土实验的文档结果在复杂的功率网上运行。
Modern algorithms in the domain of Deep Reinforcement Learning (DRL) demonstrated remarkable successes; most widely known are those in game-based scenarios, from ATARI video games to Go and the StarCraft~\textsc{II} real-time strategy game. However, applications in the domain of modern Cyber-Physical Systems (CPS) that take advantage a vast variety of DRL algorithms are few. We assume that the benefits would be considerable: Modern CPS have become increasingly complex and evolved beyond traditional methods of modelling and analysis. At the same time, these CPS are confronted with an increasing amount of stochastic inputs, from volatile energy sources in power grids to broad user participation stemming from markets. Approaches of system modelling that use techniques from the domain of Artificial Intelligence (AI) do not focus on analysis and operation. In this paper, we describe the concept of Adversarial Resilience Learning (ARL) that formulates a new approach to complex environment checking and resilient operation: It defines two agent classes, attacker and defender agents. The quintessence of ARL lies in both agents exploring the system and training each other without any domain knowledge. Here, we introduce the ARL software architecture that allows to use a wide range of model-free as well as model-based DRL-based algorithms, and document results of concrete experiment runs on a complex power grid.
