Multi-agent deep reinforcement learning for trajectory planning in UAVs-assisted mobile edge computing with heterogeneous requirements

In heterogeneous wireless networks, massive user equipments (UEs) generate computing tasks with time-varying heterogeneous requirements. To improve the service quality, this paper formulates a unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) framework for time-varying heterogeneo...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Vol. 248; p. 110469
Main Authors: Fan, Chenchen, Xu, Hongyu, Wang, Qingling
Format: Journal Article
Language:English
Published: Elsevier B.V 01.06.2024
Subjects:
Counterfactual inference
Deep reinforcement learning
Mobile edge computing
Unmanned aerial vehicle
Whale optimization algorithm
Deep reinforcement learning
Whale optimization algorithm
Unmanned aerial vehicle
Counterfactual inference
Mobile edge computing
ISSN:1389-1286, 1872-7069
Online Access:Get full text
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Summary:In heterogeneous wireless networks, massive user equipments (UEs) generate computing tasks with time-varying heterogeneous requirements. To improve the service quality, this paper formulates a unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) framework for time-varying heterogeneous task requirements. In the framework, the task delay and the number of successfully executed tasks are optimized by jointly controlling the trajectories of multiple UAVs. To address the considered trajectory planning optimization problem, a collaborative multi-agent deep reinforcement learning (MADRL) algorithm is proposed, where each UAV is regarded as a learning agent. First, a counterfactual inference based personalized policy update mechanism is proposed to evaluate the independent policy of agents by comparing the policy with a designed counterfactual policy. Based on this idea, each agent updates a personalized policy from both group and individual interests to improve its cooperation ability in dynamic and complex environments. Then, a diversified experience sampling mechanism is proposed to enhance the efficiency of policy evaluation and update with rich experiences provided by the environment interaction and the modified whale optimization algorithm. Finally, evaluation results demonstrate the superiority and effectiveness of the proposed MADRL algorithm.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2024.110469