In this letter, we consider a wireless uplink transmission scenario in which an unmanned aerial vehicle (UAV) serves as an aerial base station collecting data from ground users. To optimize the expected sum uplink transmit rate without any prior knowledge of ground users (e.g., locations, channel state information and transmit power), the trajectory planning problem is optimized via the quantum-inspired reinforcement learning (QiRL) approach. Specifically, the QiRL method adopts novel probabilistic action selection policy and new reinforcement strategy, which are inspired by the collapse phenomenon and amplitude amplification in quantum computation theory, respectively. Numerical results demonstrate that the proposed QiRL solution can offer natural balancing between exploration and exploitation via ranking collapse probabilities of possible actions, compared to the traditional reinforcement learning approaches that are highly dependent on tuned exploration parameters.

中文翻译：

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无人机无线网络中的智能轨迹规划：量子启发的强化学习视角


在这封信中，我们考虑了一种无线上行链路传输场景，其中无人机（UAV）充当空中基站，从地面用户收集数据。为了在地面用户没有任何先验知识（例如位置、信道状态信息和发射功率）的情况下优化预期总上行链路传输速率，通过量子启发强化学习（QiRL）方法来优化轨迹规划问题。具体来说，QiRL方法采用了新颖的概率动作选择策略和新的强化策略，分别受到量子计算理论中的崩溃现象和振幅放大的启发。数值结果表明，与高度依赖于调整探索参数的传统强化学习方法相比，所提出的 QiRL 解决方案可以通过对可能动作的崩溃概率进行排名来提供探索和利用之间的自然平衡。