无人机辅助的双层深度强化学习任务卸载算法

UAV-assisted two-layer deep reinforcement learning algorithm for task offloading

为了解决无人机轨迹优化、用户功率分配和任务卸载策略问题,提出了一种双层深度强化学习任务卸载算法。上层采用多智能体深度强化学习来优化无人机的轨迹,并动态分配用户的传输功率以提高网络传输速率;下层采用多个并行的深度神经网络来求解最优卸载决策以最小化网络的时延和能耗。仿真结果表明,该算法使得无人机能够跟踪用户的移动,显著降低系统的时延和能耗,能够给用户提供更优质的任务卸载服务。

In order to solve the problems of UAV trajectory optimization,user power allocation and task offloading strategy,this paper proposed a two-layer deep reinforcement learning(TDRL)algorithm for task offloading.The upper layer used the multi-agent deep reinforcement learning to optimize the trajectories of UAVs,and dynamically allocated the user transmission power to improve the transmission rate of the network.The lower layer used multiple parallel deep neural networks to generate the optimal offloading decision to minimize network latency and energy consumption.The simulation results show that the proposed algorithm enables UAVs to track user movement,significantly reduces system latency and energy consumption,and provides users with better task offloading services.