用随机超边建模多机协作，提升复杂任务下的协同效率
SDHN: Skewness-Driven Hypergraph Networks for Enhanced Localized Multi-Robot Coordination
- 用伯努利超边动态构建高阶交互网络
- 引入偏度损失使超图以小超边为主，更利于局部同步
- 适合需要精细协调的多机器人系统，如编队与仓储任务
多智能体强化学习广泛用于多机器人协同，传统图结构仅建模成对交互，难以捕捉高阶协作，限制了复杂任务中的表现。现有超图方法常生成任意结构，且对环境不确定性适应性差。为此，我们提出偏度驱动的超图网络（SDHN），通过随机伯努利超边显式建模多机器人高阶交互。引入偏度损失，促使生成以小超边为主的高效超图结构，使机器人能优先实现局部同步，同时保持整体信息一致，类比人类协作。在编队移动与机器人仓库任务上的大量实验验证了SDHN的有效性，性能优于当前最优基线。
原文摘要 · Abstract (English)
Multi-Agent Reinforcement Learning is widely used for multi-robot coordination, where simple graphs typically model pairwise interactions. However, such representations fail to capture higher-order collaborations, limiting effectiveness in complex tasks. While hypergraph-based approaches enhance cooperation, existing methods often generate arbitrary hypergraph structures and lack adaptability to environmental uncertainties. To address these challenges, we propose the Skewness-Driven Hypergraph Network (SDHN), which employs stochastic Bernoulli hyperedges to explicitly model higher-order multi-robot interactions. By introducing a skewness loss, SDHN promotes an efficient structure with Small-Hyperedge Dominant Hypergraph, allowing robots to prioritize localized synchronization while still adhering to the overall information, similar to human coordination. Extensive experiments on Moving Agents in Formation and Robotic Warehouse tasks validate SDHN's effectiveness, demonstrating superior performance over state-of-the-art baselines.
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