mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

arXiv 1907.09059 ↗First posted 22 Jul 2019cs.LG stat.MLcited by 36

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Abstract (arXiv)

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.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 5; 2023: 9; 2024: 5; 2025: 7; 2026: 3.

36 papers.

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  1. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  2. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  3. 2021Learning Long-Term Reward Redistribution via Randomized Return Decompositioncited by 33
  4. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  5. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  6. 2023On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Explorationcited by 17
  7. 2025Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Surveycited by 14
  8. 2023Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuringcited by 10
  9. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  10. 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
  11. 2021DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selectioncited by 6
  12. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
24 more
  1. 2022Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programmingcited by 4
  2. 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocationcited by 4
  3. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  4. 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
  5. 2022Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligencecited by 2
  6. 2022Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sourcescited by 2
  7. 2023Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learningcited by 2
  8. 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Controlcited by 2
  9. 2024Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchaincited by 2
  10. 2023Robust Active Measuring under Model Uncertaintycited by 1
  11. 2024Push- and Pull-based Effective Communication in Cyber-Physical Systemscited by 1
  12. 2024A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoTcited by 1
  13. 2025VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Networkcited by 1
  14. 2025Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Modelscited by 1
  15. 2021Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing Taskscited by 0
  16. 2022AVDDPG: Federated reinforcement learning applied to autonomous platoon controlcited by 0
  17. 2023Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipescited by 0
  18. 2023Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learningcited by 0
  19. 2024Patrol Security Game: Defending Against Adversary with Freedom in Attack Timing, Location, and Durationcited by 0
  20. 2025Learning Value of Information towards Joint Communication and Control in 6G V2Xcited by 0
  21. 2025A Survey of Freshness-Aware Wireless Networking with Reinforcement Learningcited by 0
  22. 2026Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarkingcited by 0
  23. 2026Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learningcited by 0
  24. 2026Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systemscited by 0