Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing

Song, Fuhong; Xing, Huanlai; Wang, Xinhan; Luo, Shouxi; Dai, Penglin; Xiao, Zhiwen

doi:10.1109/tmc.2022.3208457

Cited by 53 publications

(15 citation statements)

References 45 publications

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“…From an Environmental aspect , utilizing vehicles with high carbon footprint causes an international crisis of global warming. Though it’s unavoidable to use airplanes for transportation of products from long-distance locations, truck delivery and especially for products that are not large in terms of their size can be alternated with a green vehicle like robots and drones (Farajzadeh et al, 2020; Moadab et al, 2022 , Song et al, 2022 ). The environmental cost is not negligible, and the harmful effects of carbon emission of the operation can cause more damage to the economy and involve more people.…”

Section: Sensitivity Analysis and Discussionmentioning

confidence: 99%

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Moadab¹,

Kordi²,

Paydar³

et al. 2023

Expert Systems with Applications

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Section: Sensitivity Analysis and Discussionmentioning

confidence: 99%

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Moadab¹,

Kordi²,

Paydar³

et al. 2023

Expert Systems with Applications

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“…In both MADDPG and MAPPO, the critic network has a global view of the system, which is only applied during the training phase and actor networks are employed for each agent. An improved version of DDPG is proposed in [44] to exploit temporal correlations and improved computation cost. A major bottleneck of these algorithms is the scalability due to the shared critic network, even if the PS is considered.…”

Section: Suitability Of Marl For Ra Scheme Designmentioning

confidence: 99%

Learning Random Access Schemes for Massive Machine-Type Communication With MARL

Jadoon,

Pastore,

Navarro

et al. 2024

Trans. Mach. Learn. Comm. Netw.

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This paper investigates various multi-agent reinforcement learning (MARL) techniques for designing grant-free random access (RA) schemes suitable for low-complexity, low-power battery-operated devices in massive machine-type communication (mMTC). Previous studies on RA with MARL have shown limitations in terms of scalability and suitability for mMTC. To address scalability and practicality of the proposed methods, we examine the impact of excluding agent identification in the observation vector of each agent on network performance. We employ value decomposition networks (VDN) and QMIX algorithms with parameter sharing (PS) and compare their policies with the deep recurrent Qnetwork (DRQN). Our simulation results demonstrate that the MARL-based RA schemes can achieve a better throughput-fairness trade-off between agents without having to condition on the agent identifiers. We also present a correlated traffic model, which is more descriptive of mMTC scenarios, and show that the proposed algorithm can easily adapt to traffic non-stationarities. Moreover, the robustness of the proposed method in terms of scalability is also shown through simulations.INDEX TERMS Massive machine-type communications, MARL, reinforcement learning, grant-free random access, scalability.1 This article has been accepted for publication in IEEE Transactions on Machine Learning in Communications and Networking. This is the author's version which has not been fully edited an content may change prior to final publication.

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“…The results of the experiment demonstrate the effectiveness and superiority of the proposed algorithm with respect to existing approaches. Fuhong Song et al have proposed an approach which uses multi-objective reinforcement learning for optimizing the UAV's trajectory and offloading decisions in real-time [18]. The algorithm considers multiple objectives, including minimizing energy consumption, maximizing data processing efficiency while maintaining a stable network connection.…”

Section: Related Workmentioning

confidence: 99%

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

Khot,

Pai

et al. 2024

IJACSA

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In recent times, large applications that need near real-time processing are increasingly being used on devices with limited resources. Multi access edge computing is a computing paradigm that provides a solution to this problem by placing servers as close to resource constrained devices as possible. However, the edge device must consider multiple conflicting objectives, viz., energy consumption, latency, task drop rate and quality of experience. Many previous approaches optimize on only one objective or a fixed linear combination of multiple objectives. These approaches don't ensure best performance for applications that run on edge servers, as there is no guarantee that the solution obtained by these approaches lies on the paretofront. In this work, Multi Objective Reinforcement Learning with Actor-Critic model is proposed to optimize the drop rate, latency and energy consumption parameters during offloading decision. The model is compared with MORL-Tabular, MORL-Deep Q Network and MORL-Double Deep Q Network models. The proposed model outperforms all the other models in terms of drop rate and latency.

show abstract

Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing

Cited by 53 publications

References 45 publications

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era

Learning Random Access Schemes for Massive Machine-Type Communication With MARL

Actor Critic-based Multi Objective Reinforcement Learning for Multi Access Edge Computing

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