Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

Feriani, Amal; Hossain, Ekram

doi:10.48550/arxiv.2011.03615

Cited by 2 publications

(2 citation statements)

References 89 publications

(118 reference statements)

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“…The multi-agent reinforcement learning has not been reviewed by any of previous papers. Other papers surveyed the single agent and multi-agents RL, such as [75]- [79]. In these tutorials, the authors conducted comprehensive studies to show that the single-agent RL is not sufficient anymore to meet the requirements of emerging networks in terms of efficiency, latency, and reliability.…”

Section: Related Surveys and Paper Noveltymentioning

confidence: 99%

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour

Mhaisen

Abdellatif

et al. 2022

IEEE Commun. Surv. Tutorials

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Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes of real-time data streams. Designing accurate models using such data streams, to revolutionize the decision-taking process, inaugurates pervasive computing as a worthy paradigm for a better quality-of-life (e.g., smart homes and self-driving cars.). The confluence of pervasive computing and artificial intelligence, namely Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy and latency requirements. In this context, an intelligent resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and reinforcement learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed training and inference across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

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Section: Related Surveys and Paper Noveltymentioning

confidence: 99%

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour

Mhaisen

Abdellatif

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…However, the multi-agent online learning including Multi-Agent Reinforcement and bandit learning has not been reviewed by any one of them. Multiple papers surveyed the single agent and multi-agents reinforcement learning, such as [33]- [37]. In these tutorials, the authors conducted comprehensive studies of applications of distributed RL for networking problems and presented an overview of the evolution of cooperative and competitive MARL, in terms of rewards optimization, policy convergence, agents connection, and performance improvement.…”

Section: Ia Our Scopementioning

confidence: 99%

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Baccour,

Mhaisen,

Abdellatif

et al. 2021

Preprint

View full text Add to dashboard Cite

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-oflife. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

show abstract

Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

Cited by 2 publications

References 89 publications

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

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