Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation
Summary: arXiv:2603.23838v1 Announce Type: new
Abstract: Lifelong Multi-Agent Path Finding (MAPF) is critical for modern warehouse automation, which requires multiple robots to continuously navigate conflict-free paths to optimize the overall system throughput. However, the complexity of warehouse environments and the long-term dynamics of lifelong MAPF often demand costly adaptations to classical search-based solvers. While machine learning methods have been explored, their superiority over search-based methods remains inconclusive.
Introduction
In recent years, the automation of warehouses has become increasingly vital for improving operational efficiency and throughput. As warehouse environments grow more complex, the need for sophisticated algorithms that can manage the movement of multiple agents—such as robots—has surged. Traditional approaches to Multi-Agent Path Finding (MAPF) often struggle to adapt to the dynamic nature of these environments. This has led to the exploration of integrating machine learning techniques with classical planning methods.
RL-RH-PP Framework
This paper introduces a novel framework known as Reinforcement Learning (RL) guided Rolling Horizon Prioritized Planning (RL-RH-PP). This framework represents a significant advancement in the field of lifelong MAPF by combining the strengths of machine learning and search-based planning.
- Prioritized Planning (PP): The backbone of the RL-RH-PP framework, PP is praised for its simplicity and flexibility, allowing for the integration of a learning-based priority assignment policy.
- Dynamic Priority Assignment: By framing the priority assignment as a Partially Observable Markov Decision Process (POMDP), RL-RH-PP effectively addresses the sequential decision-making challenges inherent in lifelong planning.
- Attention-Based Neural Network: The framework utilizes an attention-based neural network that autoregressively decodes priority orders, facilitating efficient sequential single-agent planning by the PP planner.
Performance Evaluation
Extensive evaluations conducted in realistic warehouse simulations demonstrated that RL-RH-PP outperforms existing baselines, achieving the highest total throughput across diverse scenarios. The framework was tested across various metrics, including:
- Agent densities
- Planning horizons
- Warehouse layouts
Interpretive Analysis
The analyses revealed that RL-RH-PP not only enhances throughput but also proactively manages congestion among agents. By strategically redirecting agents from congested areas, the framework improves overall traffic flow within the warehouse environment.
Conclusion
The findings highlight the promising potential of integrating learning-guided approaches with traditional heuristics in modern warehouse automation. As the demand for efficient warehouse operations continues to rise, frameworks like RL-RH-PP could play a crucial role in shaping the future of automated logistics.