On Swarm Leader Identification using Probing Policies

• URL: http://arxiv.org/abs/2512.18146v1
• Date: Sat, 20 Dec 2025 00:02:58 GMT
• Title: On Swarm Leader Identification using Probing Policies
• Abstract summary: This work introduces the interactive Swarm Leader Identification problem.<n>An adversarial probing agent identifies a swarm's leader by physically interacting with its members.<n>We employ Deep Reinforcement Learning to train the prober's policy.
• Score: 2.707154152696381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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