Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requires drones not only to navigate at their limits but also to anticipate and counteract competitors' actions. In this paper, we study a fundamental question that arises in this domain: how deeply should an agent strategize before taking an action? To this end, we compare two planning paradigms: the Model Predictive Game (MPG), which finds interaction-aware strategies at the expense of longer computation times, and contouring Model Predictive Control (MPC), which computes strategies rapidly but does not reason about interactions. We perform extensive experiments to study this trade-off, revealing that MPG outperforms MPC at moderate velocities but loses its advantage at higher speeds due to latency. To address this shortcoming, we propose a Learned Model Predictive Game (LMPG) approach that amortizes model predictive gameplay to reduce latency. In both simulation and hardware experiments, we benchmark our approach against MPG and MPC in head-to-head races, finding that LMPG outperforms both baselines.

翻译：自主无人机竞速技术不断挑战高速运动规划与多智能体策略决策的极限。在这一领域取得成功不仅要求无人机以极限状态飞行，还需预测并应对竞争对手的行为。本文研究该领域的一个基本问题：智能体在采取行动前应进行多深层次的策略规划？为此，我们比较了两种规划范式：模型预测博弈（MPG）——以更长的计算时间为代价获取交互感知策略，以及轮廓模型预测控制（MPC）——快速计算策略但不对交互进行推理。我们通过大量实验研究这种权衡关系，发现MPG在中等速度下优于MPC，但在更高速度下因延迟而丧失优势。为克服这一缺陷，我们提出学习型模型预测博弈（LMPG）方法，通过分摊模型预测博弈的计算成本来降低延迟。在仿真与硬件实验中，我们将该方法与MPG和MPC在直接对抗竞速中进行基准测试，结果表明LMPG优于两种基线方法。