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

-- We propose a decentralized reinforcement learning solution for multi-agent shepherding of non-cohesive targets using policy-gradient methods. 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. The shepherding problem in robotics exemplifies the problem of harnessing complex systems for control [1], [2]. It generally involves a group of actively controlled agents, termed herders, strategically influencing a group of passive agents, referred to as targets.