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Unmanned aerial vehicles (UAVs) are seen as a promising technology to performa wide range of tasks in wireless communication networks. In this work, weconsider the deployment of a group of UAVs to collect the data generated by IoTdevices. Specifically, we focus on the case where the collected data istime-sensitive, and it is critical to maintain its timeliness. Our objective isto optimally design the UAVs' trajectories and the subsets of visited IoTdevices such as the global Age-of-Updates (AoU) is minimized. To this end, weformulate the studied problem as a mixed-integer nonlinear programming (MINLP)under time and quality of service constraints. To efficiently solve theresulting optimization problem, we investigate the cooperative Multi-AgentReinforcement Learning (MARL) framework and propose an RL approach based on thepopular on-policy Reinforcement Learning (RL) algorithm: Policy ProximalOptimization (PPO). Our approach leverages the centralized trainingdecentralized execution (CTDE) framework where the UAVs learn their optimalpolicies while training a centralized value function. Our simulation resultsshow that the proposed MAPPO approach reduces the global AoU by at least afactor of 1/2 compared to conventional off-policy reinforcement learningapproaches.

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