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

arXiv: https://arxiv.org/abs/1907.09059 · HTML: https://mlsanity.com/paper/1907.09059/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 30 of 37)

- [Federated Reinforcement Learning: Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2108.11887/) (2021)
- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [Learning Long-Term Reward Redistribution via Randomized Return Decomposition](https://mlsanity.com/paper/2111.13485/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration](https://mlsanity.com/paper/2310.16173/) (2023)
- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring](https://mlsanity.com/paper/2303.08271/) (2023)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [New Challenges in Reinforcement Learning: A Survey of Security and Privacy](https://mlsanity.com/paper/2301.00188/) (2022)
- [DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection](https://mlsanity.com/paper/2108.13157/) (2021)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming](https://mlsanity.com/paper/2206.07536/) (2022)
- [Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation](https://mlsanity.com/paper/2311.11280/) (2023)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources](https://mlsanity.com/paper/2210.07013/) (2022)
- [Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2305.00127/) (2023)
- [Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control](https://mlsanity.com/paper/2311.11281/) (2023)
- [Push- and Pull-based Effective Communication in Cyber-Physical Systems](https://mlsanity.com/paper/2401.10921/) (2024)
- [Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2409.16444/) (2024)
- [AVDDPG: Federated reinforcement learning applied to autonomous platoon control](https://mlsanity.com/paper/2207.03484/) (2022)
- [Robust Active Measuring under Model Uncertainty](https://mlsanity.com/paper/2312.11227/) (2023)
- [A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT](https://mlsanity.com/paper/2404.03276/) (2024)
- [VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network](https://mlsanity.com/paper/2505.07892/) (2025)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing Tasks](https://mlsanity.com/paper/2112.03835/) (2021)
- [Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes](https://mlsanity.com/paper/2307.09711/) (2023)
- [Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning](https://mlsanity.com/paper/2308.10124/) (2023)

## 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.