面向自主物联网的深度强化学习：模型、应用与挑战
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
摘要：
物联网（IoT）将互联网连接扩展到全球数十亿个IoT设备中，其中IoT设备收集和共享信息以反映物理世界的状态。另一方面，自主控制系统（ACS）在没有外部干预的情况下在延长的时间段内对物理系统执行控制功能。物联网和ACS的融合产生了一个新的概念-自主物联网（AIoT）。传感器收集有关系统状态的信息，基于此，物联网设备中的智能代理以及边缘/雾/云服务器为执行器做出控制决策。为了实现自主性，一种有前途的方法是智能代理利用人工智能领域的技术，特别是强化学习（RL）和深度强化学习（DRL）进行决策。在本文中，我们首先提供了一个教程的DRL，然后提出了一个通用模型的RL/DRL在AIoT中的应用。接下来，对AIoT的DRL的最新研究进行了全面的调查，其中现有的工作在所提出的通用DRL模型的保护伞下进行了分类和总结。最后，确定了未来研究的挑战和开放问题。
英文摘要：
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The sensors collect information on the system status, based on which the intelligent agents in the IoT devices as well as the Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide a tutorial of DRL, and then propose a general model for the applications of RL/DRL in AIoT. Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model. Finally, the challenges and open issues for future research are identified.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
