谁先行动？多机器人 Stackelberg 博弈中行动顺序的优化
Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots
AI总结：
针对多机器人 Stackelberg 博弈，提出 Branch and Play 算法，通过混合整数优化求解社会最优行动顺序及均衡，在空管、编队和配送中优于基线。
摘要：
我们考虑多智能体空间导航问题，即计算社会最优行动顺序（即智能体承诺其决策的序列）及其在 N 人 Stackelberg 轨迹博弈中的相关均衡。我们将此问题建模为在行动顺序排列所关联的所有可能 Stackelberg 博弈空间上的混合整数优化问题。为解决该问题，我们提出了 Branch and Play (B&P)，一种高效且精确的算法，可证明收敛到社会最优行动顺序及其 Stackelberg 均衡。作为 B&P 的子程序，我们采用并扩展了顺序轨迹规划（一种流行的多智能体控制方法），以可扩展的方式计算任意给定行动顺序下的有效局部 Stackelberg 均衡。我们展示了 B&P 在协调空中交通管制、群体编队和配送车辆车队方面的实际效用。我们发现 B&P 始终优于各种基线方法，并计算出社会最优均衡。
英文摘要：
We consider the multi-agent spatial navigation problem of computing the socially optimal order of play, i.e., the sequence in which the agents commit to their decisions, and its associated equilibrium in an N-player Stackelberg trajectory game. We model this problem as a mixed-integer optimization problem over the space of all possible Stackelberg games associated with the order of play's permutations. To solve the problem, we introduce Branch and Play (B&P), an efficient and exact algorithm that provably converges to a socially optimal order of play and its Stackelberg equilibrium. As a subroutine for B&P, we employ and extend sequential trajectory planning, i.e., a popular multi-agent control approach, to scalably compute valid local Stackelberg equilibria for any given order of play. We demonstrate the practical utility of B&P to coordinate air traffic control, swarm formation, and delivery vehicle fleets. We find that B&P consistently outperforms various baselines, and computes the socially optimal equilibrium.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
