Meta-强化学习在无线UAV网络中的轨迹设计
Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks
摘要：
研究了能量受限无人机在动态网络环境下的最优飞行轨迹设计问题。在所考虑的模型中，派遣无人机基站（DBS）为地面用户提供上行链路连接，其需求是动态的和不可预测的。在这种情况下，DBS的轨迹必须自适应地调整，以满足动态用户访问请求。为此，提出了一种元学习算法，以适应DBS的轨迹，当它遇到新的环境，通过调整强化学习（RL）的解决方案。元学习算法提供了一种解决方案，该解决方案基于有限的先前经验在新环境中快速适应DBS。元调整RL示出，以产生一个更快的收敛到最佳的覆盖率在看不见的环境中具有相当低的计算复杂度，相比基线策略梯度算法。仿真结果表明，与传统的策略梯度算法相比，该算法的收敛速度提高了25%，通信性能提高了10%左右。同时，DBS服务超过50%的用户请求的概率与基线策略梯度算法相比增加了约27%。
英文摘要：
In this paper, the design of an optimal trajectory for an energy-constrained drone operating in dynamic network environments is studied. In the considered model, a drone base station (DBS) is dispatched to provide uplink connectivity to ground users whose demand is dynamic and unpredictable. In this case, the DBS's trajectory must be adaptively adjusted to satisfy the dynamic user access requests. To this end, a meta-learning algorithm is proposed in order to adapt the DBS's trajectory when it encounters novel environments, by tuning a reinforcement learning (RL) solution. The meta-learning algorithm provides a solution that adapts the DBS in novel environments quickly based on limited former experiences. The meta-tuned RL is shown to yield a faster convergence to the optimal coverage in unseen environments with a considerably low computation complexity, compared to the baseline policy gradient algorithm. Simulation results show that, the proposed meta-learning solution yields a 25% improvement in the convergence speed, and about 10% improvement in the DBS' communication performance, compared to a baseline policy gradient algorithm. Meanwhile, the probability that the DBS serves over 50% of user requests increases about 27%, compared to the baseline policy gradient algorithm.
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