This paper provides a comprehensive literature review on the applications of deep reinforcement learning in communications and networking. With the increasing decentralization and autonomy of modern networks, such as Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, network entities need to make local decisions to maximize network performance in uncertain environments. While traditional reinforcement learning has been effective in finding optimal policies for small state and action spaces, it may struggle in complex and large-scale networks where the state and action spaces are usually large. To overcome these limitations, deep reinforcement learning, which combines reinforcement learning with deep learning, has been developed. The paper starts by providing a tutorial on deep reinforcement learning, covering fundamental concepts to advanced models. It then reviews various approaches of deep reinforcement learning that have been proposed to address emerging issues in communications and networking. The issues discussed include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation - all crucial aspects for next-generation networks like 5G and beyond. Additionally, the paper presents applications of deep reinforcement learning for traffic routing, resource sharing, and data collection.
Deep reinforcement learning is being used to solve problems in communications and networking. Traditional reinforcement learning has difficulty solving complex and large-scale network issues. Deep reinforcement learning combines reinforcement learning with deep learning to improve performance. The paper provides a tutorial on how to use deep reinforcement learning. Different methods of using deep reinforcement learning have been suggested for solving problems in communications and networking, such as dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation. Deep reinforcement learning is also used for traffic routing, resource sharing, and data collection.
Definitions
- Deep reinforcement learning: A type of machine learning that combines reinforcement learning (learning from rewards) with deep learning (a type of artificial intelligence that uses neural networks).
- Communications: The process of sending or receiving information between people or devices.
- Networking: The practice of connecting computers or other devices together so they can share information.
- Traditional: Something that has been done for a long time in a certain way.
- Complex: Something that is difficult to understand or solve because it has many parts or is very involved.
- Large-scale: Something that involves a lot of people or things.
- Tutorial: A lesson or guide that teaches someone how to do something step-by-step.
- Dynamic: Something that changes or moves constantly.
- Data rate control: Managing the speed at which information is sent or received.
- Wireless caching: Storing information temporarily on wireless devices for faster access later.
- Data offloading: Transferring
Deep Reinforcement Learning for Communications and Networking: A Comprehensive Literature Review
The increasing decentralization and autonomy of modern networks, such as Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, have necessitated the need for network entities to make local decisions in order to maximize network performance in uncertain environments. While traditional reinforcement learning has been effective in finding optimal policies for small state and action spaces, it may struggle when applied to complex and large-scale networks where the state and action space is usually large. To overcome these limitations, deep reinforcement learning has been developed which combines reinforcement learning with deep learning. This paper provides a comprehensive literature review on the applications of deep reinforcement learning in communications and networking.
Tutorial on Deep Reinforcement Learning
This section starts by providing a tutorial on deep reinforcement learning covering fundamental concepts to advanced models. It begins with an overview of basic concepts such as Markov Decision Processes (MDPs), Bellman equations, Q-learning, policy gradients methods etc., followed by a discussion on various types of neural network architectures used in deep reinforcement learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) etc.. Additionally, this section also covers recent advancements in deep reinforcement learning such as double/dueling DQN, actor-critic methods etc., which are important for understanding the applications discussed later in the paper.
Applications of Deep Reinforcement Learning
The paper then reviews various approaches of deep reinforcement learning that have been proposed to address emerging issues in communications and networking including dynamic network access control, data rate control, wireless caching strategies , data offloading mechanisms , security protocols , connectivity preservation algorithms - all crucial aspects for next-generation networks like 5G and beyond . Additionally , this section presents applications of deep RL for traffic routing , resource sharing , data collection etc .
Finally , this paper concludes with some future research directions related to applying DLRL techniques towards communication systems . These include exploring new model architectures that can better capture temporal dynamics or spatial correlations within communication systems ; developing more efficient training algorithms ; improving exploration strategies ; incorporating transfer / meta -learning into DLRL frameworks; integrating domain knowledge into DLRL models; designing distributed algorithms suitable for multi -agent settings; leveraging unsupervised / semi -supervised / self -supervised techniques; exploiting graph structures ; using generative adversarial nets ; introducing explainability into RL agents ; studying fairness considerations within RL agents; addressing privacy concerns associated with RL agents; exploring ways to reduce sample complexity while preserving accuracy etc .
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.
