Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation

2026-03-24 · Source: Journal of Artificial Intelligence Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

Reinforcement Learning (RL) guided Rolling Horizon Prioritized Planning (RL-RH-PP) is a novel framework designed for Lifelong Multi-Agent Path Finding (MAPF) in warehouse automation. This framework integrates RL with search-based planning, specifically using classical Prioritized Planning (PP) as its core. RL-RH-PP formulates dynamic priority assignment as a Partially Observable Markov Decision Process (POMDP), allowing RL to manage complex spatial-temporal interactions among agents. An attention-based neural network dynamically assigns priority orders, facilitating efficient single-agent planning. Evaluations in realistic warehouse simulations demonstrate that RL-RH-PP achieves superior total throughput compared to baseline methods and exhibits strong generalization across varying agent densities, planning horizons, and warehouse layouts. Interpretive analyses indicate that the system proactively prioritizes and redirects agents to mitigate congestion, thereby improving traffic flow and overall throughput.

Key takeaway

For AI Scientists developing multi-agent navigation systems in dynamic environments like warehouses, RL-RH-PP offers a robust approach to improve throughput and adaptability. You should consider integrating learning-based priority assignment with established search-based planners to manage complex agent interactions and generalize across diverse operational conditions. This hybrid method can proactively address congestion, leading to more efficient and scalable automation solutions.

Key insights

Integrating RL with search-based planning significantly enhances lifelong multi-agent pathfinding in complex environments.

Principles

• Prioritized planning offers simplicity and flexibility.
• Dynamic priority assignment can be modeled as a POMDP.

Method

RL-RH-PP uses an attention-based neural network to autoregressively decode priority orders for agents, enabling sequential single-agent planning by a Prioritized Planning backbone.

In practice

• Apply RL to optimize dynamic priority assignment.
• Use attention networks for sequential decision decoding.

Topics

• Lifelong MAPF
• Reinforcement Learning
• Prioritized Planning
• Warehouse Automation
• Attention Networks

Best for: AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Journal of Artificial Intelligence Research.