Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targets

Stefano Covone; Italo Napolitano; Francesco De Lellis; Mario di Bernardo

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arXiv · 2504.02479

Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targets

Stefano Covone·Italo Napolitano·Francesco De Lellis·Mario di Bernardo

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Abstract

We propose a decentralized reinforcement learning solution for multi-agent shepherding of non-cohesive targets using policy-gradient methods. Our architecture integrates target-selection with target-driving through Proximal Policy Optimization, overcoming discrete-action constraints of previous Deep Q-Network approaches and enabling smoother agent trajectories. This model-free framework effectively solves the shepherding problem without prior dynamics knowledge. Experiments demonstrate our method's effectiveness and scalability with increased target numbers and limited sensing capabilities.

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Stefano Covone, Italo Napolitano, Francesco De Lellis, Mario di Bernardo. 2025-04-03. Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targets. https://arxiv.org/abs/2504.02479

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