A Survey on Multi-Agent Reinforcement Learning Methods for Vehicular Networks

Althamary, Ibrahim; Huang, Chih‐Wei; Lin, Pengzhi

doi:10.1109/iwcmc.2019.8766739

Cited by 39 publications

(21 citation statements)

References 29 publications

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“…The learning frameworks in MADRL are also investigated. The application of MARL in solving problems for vehicular networks is studied in [99]. In [100], an overview of the evolution of cooperative MARL algorithms is presented with an emphasis on distributed optimization.…”

Section: E Multi-agent Drl Algorithmsmentioning

confidence: 99%

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions

Alwarafy

Abdallah

Çiftler

et al. 2022

IEEE Open J. Commun. Soc.

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Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value-and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.

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Section: E Multi-agent Drl Algorithmsmentioning

confidence: 99%

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions

Alwarafy

Abdallah

Çiftler

et al. 2022

IEEE Open J. Commun. Soc.

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“…Multi-Agent Deep Reinforcement Learning. There have also been many works about multi-agent deep reinforcement learning [19,20]. The multi-agent deep deterministic policy gradient (MADDPG) is proposed in [13], Lowe et al present this method for cooperative or competitive scenarios which takes the action policies of other agents into consideration.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Lou

Yang

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

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“…The learning frameworks in MADRL are also investigated. The application of MARL in solving problems for vehicular networks is studied in [85]. In [86], an overview of the evolution of cooperative MARL algorithms is presented with an emphasis on distributed optimization.…”

Section: E Multi-agent Drl Algorithmsmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

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<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

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A Survey on Multi-Agent Reinforcement Learning Methods for Vehicular Networks

Cited by 39 publications

References 29 publications

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

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