基于组分散规划的并行终身MAPF理论框架

中文摘要 AI 辅助

在终身多智能体路径查找（Lifelong Multi-Agent Path Finding，L-MAPF）问题中，智能体必须反复从一个目的地移动到另一个目的地，同时避开障碍物和智能体间的碰撞。滚动时域碰撞解决（Rolling-Horizon Collision Resolution，RHCR）框架被广泛视为该问题性能最高的解决方案之一。然而，与其高质量解决方案相对应的是，它会产生计算成本，限制了其在中等规模智能体数量下的适用性。在本文中，我们利用局部相互依赖多智能体马尔可夫决策过程（Locally Interdependent Multi-Agent MDP）文献中的理论方法，首先在L-MAPF问题的折扣马尔可夫决策过程（discounted MDP）公式中从理论上证明RHCR的近最优性。然后，我们利用这些结果自然地提出一个名为组分散RHCR（Group Decentralized RHCR，GD-RHCR）的扩展框架，该框架融入了基于传递通信方案对智能体进行划分的组分散结构，并并行规划每个智能体划分组。我们证明RHCR和GD-RHCR都能达到类似的指数级接近最优的保证，确立了普通RHCR执行的基于时间的限制与GD-RHCR执行的额外基于空间的划分之间的理论对偶性。最后，我们表明在不同的地图上，GD-RHCR能够实现高吞吐量，可扩展到更多的智能体数量，同时保持显著更低的单规划成本。

英文摘要

In the Lifelong Multi-Agent Path Finding (L-MAPF) problem, agents must repeatedly move from one destination to another while avoiding obstacles and inter-agent collisions. Widely regarded as one of the highest-performing solutions to this problem is the Rolling-Horizon Collision Resolution (RHCR) framework. However, commensurate with its quality solutions, it incurs a computational cost that limits its applicability to even modest agent counts. In this paper, leveraging theoretical methods from the Locally Interdependent Multi-Agent MDP literature, we first theoretically prove the near-optimality of RHCR in a discounted MDP formulation of the L-MAPF problem. Then, we leverage these results to naturally motivate an extended framework called Group Decentralized RHCR (GD-RHCR) which incorporates a group decentralized structure that partitions agents based on a transitive communication scheme and plans for each partition of agents in parallel. We show that both RHCR and GD-RHCR achieve similar exponentially close to optimal guarantees, establishing a theoretical duality between the time based restrictions performed by vanilla RHCR and the additional space based partitioning performed by GD-RHCR. Lastly, we show that across varying maps, GD-RHCR is able to attain high throughput that scales into higher agent counts while maintaining a significantly lower per plan cost.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。