(Open Access) Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G (2021) | Abdelkader Mekrache

Q: What contributions have the authors mentioned in the paper "Deep reinforcement learning techniques for vehicular networks: recent advances and future trends towards 6g" ?

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. In this survey, the authors first present vehicular networks and give a brief overview of RL and DRL concepts. Then the authors review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. The authors finally discuss and highlight some unresolved challenges for further study.

Q: What future works have the authors mentioned in the paper "Deep reinforcement learning techniques for vehicular networks: recent advances and future trends towards 6g" ?

Next, the authors discuss some open issues and future trends of 6G vehicular networks that deserve further investigation.

Journal ArticleDOI

Generative Adversarial Nets

Ian Goodfellow, +7 more

TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.

...read moreread less

Book

Deep Learning

Ian Goodfellow, +2 more

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

...read moreread less

Book

Reinforcement Learning: An Introduction

Richard S. Sutton, +1 more

TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.

...read moreread less

Journal ArticleDOI

Human-level control through deep reinforcement learning

Volodymyr Mnih, +18 more

- 26 Feb 2015 -

Nature

TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

...read moreread less

Proceedings Article

Asynchronous methods for deep reinforcement learning

Volodymyr Mnih, +7 more

TL;DR: A conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers and shows that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.

...read moreread less

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Figures

Citations

Machine Learning for Next-Generation Intelligent Transportation Systems: A Survey

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Artificial Intelligence techniques to mitigate cyber-attacks within vehicular networks: Survey

Deep Reinforcement Learning based Resource Allocation in Dense Sliced LoRaWAN Networks

Towards the Age of Intelligent Vehicular Networks for Connected and Autonomous Vehicles in 6G

References

Generative Adversarial Nets

Deep Learning

Reinforcement Learning: An Introduction

Human-level control through deep reinforcement learning

Asynchronous methods for deep reinforcement learning

Related Papers (5)

Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Toward Intelligent Vehicular Networks: A Machine Learning Framework

Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey

Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks

Frequently Asked Questions (2)

Q1. What contributions have the authors mentioned in the paper "Deep reinforcement learning techniques for vehicular networks: recent advances and future trends towards 6g" ?

Q2. What future works have the authors mentioned in the paper "Deep reinforcement learning techniques for vehicular networks: recent advances and future trends towards 6g" ?