HIPPO-MAT：使用GraphSAGE与多智能体深度强化学习的去中心化任务分配
HIPPO-MAT: Decentralized Task Allocation Using GraphSAGE and Multi-Agent Deep Reinforcement Learning
AI总结：
本文提出HIPPO-MAT框架，结合GraphSAGE图神经网络与IPPO多智能体深度强化学习，实现异构多智能体系统在3D环境中的去中心化、成本最优且冲突感知的任务分配，并通过仿真和真实机器人实验验证其可扩展性与实用性。
摘要：
本文研究异构多智能体系统中的去中心化连续任务分配问题。我们提出了一个新颖的框架HIPPO-MAT，该框架将采用GraphSAGE架构的图神经网络（GNN）与用于多智能体深度强化学习的独立近端策略优化（IPPO）方法相结合，在每个智能体上计算独立的嵌入表示。在我们的系统中，无人机（UAV）和无人地面车辆（UGV）通过通信信道共享聚合的观测数据，同时独立处理这些输入以生成增强的状态嵌入。该设计能够在3D网格环境中实现动态、成本最优且具有冲突感知能力的任务分配，而无需集中式协调。系统集成了改进的A*路径规划器，用于高效路径规划和碰撞避免。仿真实验证明了该方法在多达30个智能体下的可扩展性，并在JetBot ROS AI机器人上进行了初步的真实世界验证，每个机器人在Jetson Nano上运行其模型，并通过使用ESP32-S3的ESP-NOW协议进行通信，这证实了该方法的实际可行性，该方法同时结合了同步定位与建图（SLAM）。实验结果表明，我们的方法实现了92.5%的高无冲突成功率，与集中式匈牙利方法相比性能差距仅为16.49%，同时优于基于贪心策略的启发式去中心化基线。此外，该框架在多达30个智能体的情况下表现出可扩展性，分配处理时间为0.32个仿真步长时间，并且在响应动态生成的任务方面具有鲁棒性。
英文摘要：
This paper tackles decentralized continuous task allocation in heterogeneous multi-agent systems. We present a novel framework HIPPO-MAT that integrates graph neural networks (GNN) employing a GraphSAGE architecture to compute independent embeddings on each agent with an Independent Proximal Policy Optimization (IPPO) approach for multi-agent deep reinforcement learning. In our system, unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) share aggregated observation data via communication channels while independently processing these inputs to generate enriched state embeddings. This design enables dynamic, cost-optimal, conflict-aware task allocation in a 3D grid environment without the need for centralized coordination. A modified A* path planner is incorporated for efficient routing and collision avoidance. Simulation experiments demonstrate scalability with up to 30 agents and preliminary real-world validation on JetBot ROS AI Robots, each running its model on a Jetson Nano and communicating through an ESP-NOW protocol using ESP32-S3, which confirms the practical viability of the approach that incorporates simultaneous localization and mapping (SLAM). Experimental results revealed that our method achieves a high 92.5% conflict-free success rate, with only a 16.49% performance gap compared to the centralized Hungarian method, while outperforming the heuristic decentralized baseline based on greedy approach. Additionally, the framework exhibits scalability with up to 30 agents with allocation processing of 0.32 simulation step time and robustness in responding to dynamically generated tasks.
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。
