Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Authors: Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim
Abstract
This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.
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Closest papers
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Applications of Deep Reinforcement Learning in Wireless Networks-A Recent ReviewAbdelhamid Archi, H. Saadi, S. Mekaoui · 2023 · 2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM) · cited 9 times · Not summarized yet
Abstract
Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent years for future wireless networks. They can overcome the ever-increasing challenges of wireless environment and limitations of traditional techniques. Some applications of DRL for future wireless and mobile networks are network optimization, network control, power management, mobile edge computation (MEC), offloading, edge cachings, network slicing and many others. This paper mainly addresses network optimization specifically energy optimization, and secondly resource allocation. A recent literature review is presented by looking at research so far achieved, DRL methods used, and future research directi…
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An Overview of Intelligent Wireless Communications using Deep Reinforcement LearningYongming Huang, Chunmei Xu, Cheng Zhang et al. · 2019 · J. Commun. Inf. Networks · cited 44 times · Not summarized yet
Abstract
Future wireless communication networks tend to be intelligentized to accomplish the missions that cannot be preprogrammed. In the new intelligent communication systems, optimizing the network performance has become a challenge due to the ever-increasing complexity of the network environment. New theories and technologies for intelligent wireless communications have obtained widespread attention, among which deep reinforcement learning (DRL) is an excellent machine learning technology. DRL has great potential in enhancing the intelligence of wireless communication systems while overcoming the above challenge. This paper presents a review on applications of DRL in intelligent wireless communic…
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Leveraging Deep Reinforcement Learning for Traffic Engineering: A SurveyYang Xiao, Jun Liu, Jiawei Wu et al. · 2021 · IEEE Communications Surveys and Tutorials · cited 127 times · Not summarized yet
Abstract
After decades of unprecedented development, modern networks have evolved far beyond expectations in terms of scale and complexity. In many cases, traditional traffic engineering (TE) approaches fail to address the quality of service (QoS) requirements of modern networks. In recent years, deep reinforcement learning (DRL) has proved to be a feasible and effective solution for autonomously controlling and managing complex systems. Massive growth in the use of DRL applications in various domains is beginning to benefit the communications industry. In this paper, we firstly provide a comprehensive overview of DRL-based TE. Then, we present a detailed literature review on applications of DRL for …
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Deep Reinforcement Learning for Autonomous Communication Networks: Resource Allocation, Spectrum Management, and ControlD. A. Kumar, Jakkula Rakshitha, Madugula Pranush · 2026 · International Scientific Journal of Engineering and Management · Not summarized yet
Abstract
ABSTRACT Autonomous communication systems are evolving toward self-organizing, adaptive networks capable of optimizing performance under dynamic and uncertain environments. Traditional rule-based and model-driven optimization techniques struggle to cope with the complexity, scale, and non-stationarity of modern wireless and networked systems. Reinforcement learning (RL), a branch of machine learning where agents learn optimal policies through interaction with the environment, has emerged as a powerful paradigm for enabling autonomy in communication systems. This paper (or study) explores the application of reinforcement learning techniques to autonomous communication networks, including reso…
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Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6GAbdelkader Mekrache, Abbas Bradai, Emmanuel Moulay et al. · 2021 · Vehicular Communications · cited 109 times · Not summarized yet
Abstract
Abstract Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performan…
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Reinforcement Learning Meets Wireless Networks: A Layering PerspectiveYawen Chen, Yu Liu, Ming Zeng et al. · 2021 · IEEE Internet of Things Journal · cited 34 times · Not summarized yet
Abstract
Driven by the soaring traffic demand and the growing diversity of mobile services, wireless networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such large-scale and complicated wireless networks, optimal controlling is reaching unprecedented levels of complexity while its traditional solutions of handcrafted offline algorithms become inefficient due to high complexity, low robustness, and high overhead. Therefore, reinforcement learning (RL), which enables network entities to learn from their actions and consequences in the interactive network environment, attracts significant attention. In this article, we comprehensively review the applications of RL in wirele…
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Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive SurveyTian-Xu Li, Kun Zhu, Luong Cong Nguyen et al. · 2021 · IEEE Communications Surveys and Tutorials · cited 264 times · Not summarized yet
Abstract
Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, the future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the un…
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A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning ApproachesProhim Tam, Seyha Ros, Inseok Song et al. · 2024 · Electronics · cited 46 times · Not summarized yet
Abstract
This paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its variants, and the state-of-the-art applications in communication networking, which reveal the potential to revolutionize access, transport, and core network management policies. This paper further explores DRL capabilities, its variants, and the trending applications in E2E networking, particularly in enhancing dynamic network (re)configurations and resource management. By fusing GNN with DRL, we spotlight novel approaches, ranging from radio access networks to core management an…
Prior work it builds on
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Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning ApproachYing He, Nan Zhao, Hongxi Yin · 2018 · IEEE Transactions on Vehicular Technology · cited 498 times · Not summarized yet
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Cellular Network Traffic Scheduling With Deep Reinforcement LearningSandeep P. Chinchali, Pan Hu, Tian-Shu Chu et al. · 2018 · AAAI Conference on Artificial Intelligence · cited 202 times · Not summarized yet
Abstract
Modern mobile networks are facing unprecedented growth in demand due to a new class of traffic from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional real-time applications such as voice-calling and video. This problem is extremely challenging because conventional traffic is highly dynamic across space and time, so its performance is significantly impacted if all IoT traffic is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler tha…
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Deep Reinforcement Learning for Distributed Dynamic Power Allocation in Wireless NetworksYasar Sinan Nasir, Dongning Guo · 2018 · Signal Processing · cited 603 times · AI summary available
Abstract
This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in emerging and future wireless networks. Various techniques have been proposed in the literature to find near-optimal power allocations, often by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a model-free distributed dynamic power allocation scheme is developed based on deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from seve…
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UAV-Aided Cellular Communications with Deep Reinforcement Learning Against JammingXiao-Zhen Lu, Liang Xiao, Canhuang Dai et al. · 2018 · IEEE wireless communications · cited 116 times · Not summarized yet
Abstract
Cellular systems have to resist smart jammers that can optimize their selection of jamming channels and powers based on the estimated ongoing network states. In this article, we present an unmanned aerial vehicle (UAV) aided cellular framework against jamming, in which an UAV uses reinforcement learning to choose the relay policy for a mobile user whose serving base station is attacked by a jammer. More specifically, the UAV applies deep reinforcement learning and transfer learning to help cellular systems resist smart jamming without knowing the cellular topology, the message generation model, the server computation model and the jamming model, based on the previous anti-jamming relay exper…
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Deep Learning for Intelligent Wireless Networks: A Comprehensive SurveyQian Mao, Fei Hu, Qi Hao · 2018 · IEEE Communications Surveys and Tutorials · cited 734 times · Not summarized yet
Papers that cite it
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Multi-agent deep reinforcement learning: a surveySven Gronauer, Klaus J. Diepold · 2021 · Artificial Intelligence Review · cited 989 times · Not summarized yet
Abstract
Abstract The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario. To survey the works that constitute the contemporary landscape, the main contents are divided into three parts. …
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A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworksS. Mahmud, A. Sayed, Yassine Himeur et al. · 2026 · Renewable & Sustainable Energy Reviews · cited 10 times · Not summarized yet
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Deep Reinforcement Learning for Cyber SecurityThanh Thi Nguyen, Vijay Janapa Reddi · 2019 · IEEE Transactions on Neural Networks and Learning Systems, 2021 (Early Access) · cited 696 times · AI summary available
Abstract
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for…
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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication NetworksLuong Cong Nguyen, Shao-Han Feng, H. D. Nguyen et al. · 2026 · IEEE Communications Surveys and Tutorials · cited 1 time · Not summarized yet
Abstract
Reinforcement Learning (RL) has long been a powerful solution to various problems in communication networks. However, traditional RL models still face several limitations. Not only do they rely on large numbers of interactions with the environment, but they are also limited in terms of modeling long-term relationships and tackling partial observability. In recent years, the Transformer model has demonstrated the ability to enhance RL models, allowing them to overcome these issues. Particularly, the self-attention mechanism within the Transformer enables efficient modeling of long-range dependencies and global correlations, as well as accelerates training processes and handles heterogeneous d…
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Types and Challenges of Resource Allocation for Reinforcement Learning in Wireless Networks: A ReviewOmar Khalid M. Ali, Muhamad Azhar Abdilatef Alobaidy, Z. Yosif et al. · 2026 · International Journal of Computer Networks And Applications · Not summarized yet
Abstract
– The allocation of resources is one of the primary issues in wireless networks, as it has a direct impact on spectrum efficiency, energy efficiency, latency, and Quality-of-Service (QoS) provisioning. The growing complexity and heterogeneity of the current state of wireless environment, such as dense deployments and time varying channels, have revealed weaknesses in the traditional optimization-based approaches to assigning resources, which usually assume a simplified model and require perfect knowledge of the entire system. This has led to reinforcement learning (RL), which has become a promising alternative because it is able to learn adaptive decision-making policies by interacting with …
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Updated on 5 November 2026.