基于局部信息聚合的多智能体强化学习在机器人群动态任务分配中的应用
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation
AI总结：
针对动态环境下机器人群的任务分配优化问题，提出融合CTDE的LIA_MADDPG算法，通过LIA模块提升决策效率，实证表明其可无缝集成到多种MARL方法中，且在多维度性能上优于传统算法。
摘要：
本文探讨了如何在动态环境中优化机器人群的任务分配，强调为机器人协作制定鲁棒、灵活且可扩展策略的必要性。我们引入了一个基于分散式部分可观测马尔可夫决策过程（Dec_POMDP）的新型框架，专门为分布式机器人群网络设计。我们方法的核心是局部信息聚合多智能体深度确定性策略梯度（LIA_MADDPG）算法，该算法融合了集中式训练与分布式执行（CTDE）。在集中式训练阶段，精心设计了一个局部信息聚合（LIA）模块，用于收集相邻机器人的关键数据，以提高决策效率。在分布式执行阶段，提出了一种策略改进方法，可根据变化的、部分可观测的环境条件动态调整任务分配。我们的实证评估表明，LIA模块可无缝集成到多种基于CTDE的多智能体强化学习（MARL）方法中，显著提升其性能。此外，通过将LIA_MADDPG与六种传统强化学习算法和一种启发式算法进行比较，我们证明了其在可扩展性、快速适应环境变化以及保持稳定性和收敛速度方面的优越性。这些结果突显了LIA_MADDPG的卓越性能及其通过增强局部协作和自适应策略执行显著改善机器人群动态任务分配的潜力。
英文摘要：
In this paper, we explore how to optimize task allocation for robot swarms in dynamic environments, emphasizing the necessity of formulating robust, flexible, and scalable strategies for robot cooperation. We introduce a novel framework using a decentralized partially observable Markov decision process (Dec_POMDP), specifically designed for distributed robot swarm networks. At the core of our methodology is the Local Information Aggregation Multi-Agent Deep Deterministic Policy Gradient (LIA_MADDPG) algorithm, which merges centralized training with distributed execution (CTDE). During the centralized training phase, a local information aggregation (LIA) module is meticulously designed to gather critical data from neighboring robots, enhancing decision-making efficiency. In the distributed execution phase, a strategy improvement method is proposed to dynamically adjust task allocation based on changing and partially observable environmental conditions. Our empirical evaluations show that the LIA module can be seamlessly integrated into various CTDE-based MARL methods, significantly enhancing their performance. Additionally, by comparing LIA_MADDPG with six conventional reinforcement learning algorithms and a heuristic algorithm, we demonstrate its superior scalability, rapid adaptation to environmental changes, and ability to maintain both stability and convergence speed. These results underscore LIA_MADDPG's outstanding performance and its potential to significantly improve dynamic task allocation in robot swarms through enhanced local collaboration and adaptive strategy execution.
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。
