基于任务语义的分布式智能体网络用于水下目标跟踪
Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking
AI总结：
本文提出STG-MAPPO算法，通过整合DI-engine与六自由度水下AUV仿真器，构建开放平台评估不同MARL算法，解决多智能体强化学习在水下目标跟踪中的挑战。
摘要：
自主水下航行器（AUV）群正在成为智能水下网络，其中每个节点必须在严峻的声学约束下感知、通信、处理本地数据并做出决策。持久性的水下目标跟踪是典型的任务，具有移动目标、变化的通信拓扑、间歇性声学链路和每个AUV有限的观测。多智能体强化学习（MARL）是分布式跟踪的自然候选者，但现有研究仍缺乏一个统一的开源平台来评估不同MARL算法在六自由度AUV动态下的性能。此外，使用原始几何状态和低层力动作训练的策略往往难以表示任务阶段、观测可靠性、链路质量以及局部合作角色。本文通过开发一个整合DI-engine与六自由度水下AUV目标跟踪仿真的开源MARL-AUV平台来解决这些问题。据我们所知，这是第一个将公共MARL训练框架与物理建模的AUV群任务连接起来的开源平台，并提供统一的实验协议，用于公平训练、测试和比较代表性RL和MARL算法。基于此平台，我们提出了STG-MAPPO，一种增强的多智能体近端策略优化变种。STG-MAPPO从跟踪诊断、任务阶段、观测置信度、链路可用性、邻居跟踪质量以及局部角色优势构建语义策略输入。一个紧凑的语义任务图将通信受限的网络状态连接到去中心化的动作决策，而速度级动作抽象将高层协作决策映射到可执行的六自由度AUV控制输入。代码可在https://github.com/dasjsaj/MARL-AUV获取。
英文摘要：
Autonomous underwater vehicle (AUV) swarms are emerging as intelligent underwater networks, where each node must sense, communicate, process local data, and make decisions under severe acoustic constraints. Persistent underwater target tracking is a typical task with moving targets, changing communication topology, intermittent acoustic links, and limited observation for each AUV. Multi-agent reinforcement learning (MARL) is a natural candidate for distributed tracking, yet existing studies still lack a unified open-source platform for evaluating different MARL algorithms under six-degree-of-freedom AUV dynamics. In addition, policies trained with raw geometric states and low-level force actions often struggle to represent task phases, observation reliability, link quality, and local cooperation roles. This paper addresses these issues by developing an open-source MARL-AUV platform that integrates DI-engine with a six-degree-of-freedom underwater AUV target-tracking simulator. To the best of our knowledge, it is the first open platform that connects a public MARL training framework with physically modeled AUV swarm-based tasks, and provides a unified experimental protocol for fair training, testing, and comparison of representative RL and MARL algorithms. Based on this platform, we propose STG-MAPPO, a Semantic Task Graph-enhanced variant of Multi-Agent Proximal Policy Optimization. STG-MAPPO builds semantic policy inputs from tracking diagnostics, task phases, observation confidence, link availability, neighbor tracking quality, and local role advantage. A compact semantic task graph links communication-constrained network states to decentralized actor decisions, and a velocity-level action abstraction maps high-level cooperative decisions to executable six-degree-offreedom AUV control inputs.The code is available at https://github.com/dasjsaj/MARL-AUV.
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。
