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：自主无人机竞速推动了高速运动规划与多智能体战略决策的极限。在这一领域，成功不仅要求无人机以极限速度飞行，还需预测并反制对手的动作。本文研究了该领域的一个基本问题：智能体在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）能够生成交互感知策略，但计算时间较长；而轮廓模型预测控制（MPC）能快速计算策略，但不考虑交互作用。通过大量实验研究这一权衡，我们发现MPG在中速情况下优于MPC，但在高速情况下因延迟而丧失优势。针对这一缺陷，我们提出了一种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来降低延迟。在仿真与硬件实验中，我们通过一对一竞速将所提方法与MPG及MPC进行对比，发现LMPG在两种基准方法中均表现更优。