基于宏观动作的深度强化学习用于去中心化多机器人探索
Deep Reinforcement Learning for Decentralized Multi-Robot Exploration With Macro Actions
AI总结：
本文提出首个宏观动作去中心化探索网络MADE-Net，利用多智能体深度强化学习解决通信中断下的多机器人探索问题，在计算时间、行驶距离、交互次数和探索率上均优于基准方法，并展现出良好的可扩展性。
摘要：
协作式多机器人团队需要能够在杂乱且非结构化的环境中进行探索，同时应对通信中断问题，因为通信中断会阻止它们交换局部信息以维持团队协调。因此，机器人在动作选择过程中需要考虑队友的高层意图。在本文中，我们提出了首个宏观动作去中心化探索网络（MADE-Net），利用多智能体深度强化学习（DRL）来应对在未知、非结构化和杂乱环境中多机器人探索时通信中断的挑战。我们进行了模拟机器人团队探索实验，并与经典方法和DRL方法进行了对比，结果表明MADE-Net在计算时间、总行驶距离、机器人之间的局部交互次数以及不同通信中断程度下的探索率方面均优于所有基准方法。在3D环境中进行的可扩展性研究表明，随着团队规模和环境规模的增大，MADE-Net的探索时间有所减少。所展示的实验突出了我们方法的有效性和鲁棒性。
英文摘要：
Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments while dealing with communication dropouts that prevent them from exchanging local information to maintain team coordination. Therefore, robots need to consider high-level teammate intentions during action selection. In this letter, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning (DRL) to address the challenges of communication dropouts during multi-robot exploration in unseen, unstructured, and cluttered environments. Simulated robot team exploration experiments were conducted and compared against classical and DRL methods where MADE-Net outperformed all benchmark methods in terms of computation time, total travel distance, number of local interactions between robots, and exploration rate across various degrees of communication dropouts. A scalability study in 3D environments showed a decrease in exploration time with MADE-Net with increasing team and environment sizes. The experiments presented highlight the effectiveness and robustness of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
