# Enhancing PIBT via Multi-Action Operations

arXiv: https://arxiv.org/abs/2511.09193 · HTML: https://mlsanity.com/paper/2511.09193/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2025.

## Built on by (most cited first, 5 of 5)

- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [Planning over MAPF Agent Dependencies via Multi-Dependency PIBT](https://mlsanity.com/paper/2603.23405/) (2026)
- [Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations](https://mlsanity.com/paper/2608.05588/) (2026)
- [GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding](https://mlsanity.com/paper/2512.03194/) (2025)
- [Scalable Long-Horizon Planning with Staggered Updates for Lifelong MAPF](https://mlsanity.com/paper/2608.06702/) (2026)

## Abstract (arXiv)

PIBT is a rule-based Multi-Agent Path Finding (MAPF) solver, widely used as a low-level planner or action sampler in many state-of-the-art approaches. Its primary advantage lies in its exceptional speed, enabling action selection for thousands of agents within milliseconds by considering only the immediate next timestep. However, this short-horizon design leads to poor performance in scenarios where agents have orientation and must perform time-consuming rotation actions. In this work, we present an enhanced version of PIBT that addresses this limitation by incorporating multi-action operations. We detail the modifications introduced to improve PIBT's performance while preserving its hallmark efficiency. Furthermore, we demonstrate how our method, when combined with graph-guidance technique and large neighborhood search optimization, achieves state-of-the-art performance in the online LMAPF-T setting.