基于分布式策略进化的大规模仓库多智能体寻径强化学习

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Qinru Shi;Meiqin Liu;Senlin Zhang;Xuguang Lan
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引用次数: 0

摘要

高效的多智能体路径搜索(MAPF)是大规模仓储物流系统的关键。尽管强化学习(RL)方法具有潜力,但目前的方法面临着探索效率低下、泛化能力差和解决死锁不足等挑战。为了解决这些问题,我们提出了一种新的进化强化学习(ERL)框架来解决大规模仓库环境中的MAPF问题。具体而言,该框架利用分布式政策演化方法提供多样化的经验,从而提高政策培训效率和政策绩效。我们进一步将课程学习整合到该框架中,以提高策略的通用性,并使其可扩展到更大的环境中。此外,我们还引入了一种基于专家经验的死锁打破机制,帮助缓解大规模和高密度场景中的死锁问题。实验表明,我们的方法在各种环境中都优于现有的方法,特别是在拥有超过1000个代理的复杂场景中表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcement Learning for Multi-Agent Path Finding in Large-Scale Warehouses via Distributed Policy Evolution
Efficient multi-agent path finding (MAPF) is essential for large-scale warehousing and logistics systems. Despite the potential of reinforcement learning (RL) methods, current approaches struggle with challenges such as inefficient exploration, poor generalization and inadequate deadlock resolution. To address these issues, we propose a novel evolutionary reinforcement learning (ERL) framework to address the MAPF problem in large-scale warehouse environments. Specifically, the framework leverages distributed policy evolution methods to provide diverse experiences, thereby improving policy training efficiency and policy performance. We further integrate curriculum learning into this framework to improve the generality of the policy and make it scalable to larger environments. Additionally, we introduce a deadlock-breaking mechanism based on expert experience, helping to mitigate deadlock issues in large-scale and high-density scenarios. Experiments show that our method outperforms existing methods across various environments, particularly excelling in complex scenarios with over 1,000 agents.
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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