Keisuke Okumura, Hiroki Nagai

Lightweight and Effective Preference Construction in PIBT for Large-Scale Multi-Agent Pathfinding

Introduction

Lightweight and effective preference construction in pibt for large-scale multi-agent pathfinding. Enhance large-scale Multi-Agent Pathfinding (MAPF) with improved PIBT tiebreaking techniques. Reduce solution cost and boost throughput by 10-20% in dense scenarios without sacrificing speed.

Abstract

PIBT is a computationally lightweight algorithm that can be applied to a variety of multi-agent pathfinding (MAPF) problems, generating the next collision-free locations of agents given another. Because of its simplicity and scalability, it is becoming a popular underlying scheme for recent large-scale MAPF methods involving several hundreds or thousands of agents. Vanilla PIBT makes agents behave greedily towards their assigned goals, while agents typically have multiple best actions, since the graph shortest path is not always unique. Consequently, tiebreaking about how to choose between these actions significantly affects resulting solutions. This paper studies two simple yet effective techniques for tiebreaking in PIBT, without compromising its computational advantage. The first technique allows an agent to intelligently dodge another, taking into account whether each action will hinder the progress of the next timestep. The second technique is to learn, through multiple PIBT runs, how an action causes regret in others and to use this information to minimise regret collectively. Our empirical results demonstrate that these techniques can reduce the solution cost of one-shot MAPF and improve the throughput of lifelong MAPF. For instance, in densely populated one-shot cases, the combined use of these tiebreaks achieves improvements of around 10-20% in sum-of-costs, without significantly compromising the speed of a PIBT-based planner.

Review

This paper presents a valuable contribution to the field of multi-agent pathfinding (MAPF), specifically focusing on improving the performance of PIBT, a computationally lightweight and scalable algorithm often employed in large-scale scenarios. The authors astutely identify a key limitation of vanilla PIBT: its greedy nature often results in multiple equally "best" actions for an agent, where tiebreaking decisions can profoundly impact the quality of the overall solution. To address this, the work introduces two novel yet simple techniques designed to make these tiebreaking choices more intelligent and effective, crucially without compromising PIBT's inherent computational efficiency. The two proposed techniques offer distinct approaches to enhancing PIBT's decision-making. The first method introduces an "intelligent dodging" strategy, enabling agents to consider not just their immediate goal but also the potential for their chosen action to hinder other agents' progress in subsequent timesteps. This forward-looking local optimization adds a critical layer of foresight. The second technique leverages learning across multiple PIBT runs to quantify how specific actions cause "regret" in other agents, and subsequently uses this insight to minimize collective regret. Empirical results underscore the efficacy of these enhancements, demonstrating substantial improvements in solution quality. For instance, in challenging dense one-shot MAPF scenarios, the combined use of these tiebreaks yields a significant 10-20% reduction in sum-of-costs, alongside improved throughput in lifelong MAPF. The contributions of this paper are highly relevant and impactful for the MAPF community, particularly given the increasing demand for scalable solutions in complex environments. By providing practical and computationally efficient methods to refine tiebreaking within PIBT, the authors have significantly boosted the performance of an already popular and effective algorithm. The emphasis on maintaining computational lightness is a major strength, ensuring that these improvements are directly applicable to the large-scale problems where PIBT excels. This work offers a compelling and well-supported advancement that enhances the robustness and solution quality of PIBT-based planners, making them even more viable for real-world multi-agent coordination challenges.

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