Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
Abstract
This paper presents a novel approach to robot swarm coordination control utilizing Multi-Agent Reinforcement Learning (MARL). The core claim is to design a robust and adaptive robot swarm control system by leveraging the learning capabilities inherent in MARL. The proposed system employs a federated reinforcement learning architecture, where each robot is controlled by an individual agent. These agents learn through both information sharing and competitive interactions. The key innovation lies in replacing traditional rule-based or model-based control methods with a learning-based framework, enabling effective solutions to complex collaborative tasks in dynamic and uncertain environments. The system's design focuses on achieving efficient coordination and adaptability, ultimately leading to improved performance in challenging scenarios. This work provides a foundation for developing truly intelligent and self-organizing robot swarms. ---
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