基于探测策略的蜂群领头者识别
On Swarm Leader Identification using Probing Policies
AI总结：
本文提出了一种基于探测策略的蜂群领头者识别方法，利用TGR层和S5模型实现高效的领头者识别，并通过仿真和实际实验验证了其在对抗环境中的有效性。
摘要：
在机器人蜂群中识别领头者至关重要，尤其是在对抗性情境中，隐藏领头者对于任务成功是必要的。本文引入了交互式蜂群领头者识别（iSLI）问题，这是一种新的方法，其中对抗性探测代理通过与成员物理互动来识别蜂群的领头者。我们将iSLI问题建模为部分可观测马尔可夫决策过程（POMDP），并采用深度强化学习，特别是近端策略优化（PPO），来训练探测器的策略。所提出的方法利用了一种新颖的神经网络架构，其中包括一个带有时间图关系former（TGR）层和简化结构状态空间序列（S5）模型的神经网络。TGR层有效处理蜂群的图观测，捕捉时间依赖性，并利用学习的门控机制融合关系信息，生成用于策略学习的信息性表示。大量模拟显示，我们的基于TGR的模型在基线图神经网络架构上表现更优，并且在不同规模和速度的蜂群中表现出显著的零样本泛化能力。训练后的探测器在识别领头者方面具有高准确性，在训练分布之外的场景中保持性能，并在预测中表现出适当的信心水平。使用物理机器人的实际实验进一步验证了该方法，确认了仿真到现实的转移以及对动态变化的鲁棒性，例如意外代理断开。
英文摘要：
Identifying the leader within a robotic swarm is crucial, especially in adversarial contexts where leader concealment is necessary for mission success. This work introduces the interactive Swarm Leader Identification (iSLI) problem, a novel approach where an adversarial probing agent identifies a swarm's leader by physically interacting with its members. We formulate the iSLI problem as a Partially Observable Markov Decision Process (POMDP) and employ Deep Reinforcement Learning, specifically Proximal Policy Optimization (PPO), to train the prober's policy. The proposed approach utilizes a novel neural network architecture featuring a Timed Graph Relationformer (TGR) layer combined with a Simplified Structured State Space Sequence (S5) model. The TGR layer effectively processes graph-based observations of the swarm, capturing temporal dependencies and fusing relational information using a learned gating mechanism to generate informative representations for policy learning. Extensive simulations demonstrate that our TGR-based model outperforms baseline graph neural network architectures and exhibits significant zero-shot generalization capabilities across varying swarm sizes and speeds different from those used during training. The trained prober achieves high accuracy in identifying the leader, maintaining performance even in out-of-training distribution scenarios, and showing appropriate confidence levels in its predictions. Real-world experiments with physical robots further validate the approach, confirming successful sim-to-real transfer and robustness to dynamic changes, such as unexpected agent disconnections.
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。
