Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks

• URL: http://arxiv.org/abs/2303.08680v1
• Date: Wed, 15 Mar 2023 15:03:09 GMT
• Title: Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks
• Authors: Mouhamed Naby Ndiaye, El Houcine Bergou, Hajar El Hammouti
• Abstract summary: We focus on the case where the collected data is time-sensitive, and it is critical to maintain its timeliness. Our objective is to optimally design the UAVs' trajectories and the subsets of visited IoT devices such as the global Age-of-Updates (AoU) is minimized.
• Score: 4.042622147977782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned aerial vehicles (UAVs) are seen as a promising technology to perform a wide range of tasks in wireless communication networks. In this work, we consider the deployment of a group of UAVs to collect the data generated by IoT devices. Specifically, we focus on the case where the collected data is time-sensitive, and it is critical to maintain its timeliness. Our objective is to optimally design the UAVs' trajectories and the subsets of visited IoT devices such as the global Age-of-Updates (AoU) is minimized. To this end, we formulate the studied problem as a mixed-integer nonlinear programming (MINLP) under time and quality of service constraints. To efficiently solve the resulting optimization problem, we investigate the cooperative Multi-Agent Reinforcement Learning (MARL) framework and propose an RL approach based on the popular on-policy Reinforcement Learning (RL) algorithm: Policy Proximal Optimization (PPO). Our approach leverages the centralized training decentralized execution (CTDE) framework where the UAVs learn their optimal policies while training a centralized value function. Our simulation results show that the proposed MAPPO approach reduces the global AoU by at least a factor of 1/2 compared to conventional off-policy reinforcement learning approaches.

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