Decentralized Shepherding of Non-Cohesive Swarms Through Cluttered Environments via Deep Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
This paper investigates decentralized shepherding in cluttered environments, where a limited number of herders must guide a larger group of non-cohesive, diffusive targets toward a goal region in the presence of static obstacles. A hierarchical control architecture is proposed, integrating a high-level target assignment rule, where each herder is paired with a selected target, with a learning-based low-level driving module that enables effective steering of the assigned target. The low-level policy is trained in a one-herder-one-target scenario with a rectangular obstacle using Proximal Policy Optimization and then directly extended to multi-agent settings with multiple obstacles without requiring retraining. Numerical simulations demonstrate smooth, collision-free trajectories and consistent convergence to the goal region, highlighting the potential of reinforcement learning for scalable, model-free shepherding in complex environments.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 26 references that are in the map. First posted on arXiv 26 Nov 2025.
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11 of 26 references.
References · 11
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2019Control Barrier Functions: Theory and Applicationscited by 372
- 2019A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approachcited by 8
- 2015Invisible control of self-organizing agents leaving unknown environmentscited by 7
- 2020Herding stochastic autonomous agents via local control rules and online global target selection strategiescited by 5
- 2020Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learningcited by 4
- 2023Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environmentscited by 2
- 2024Emergent Cooperative Strategies for Multi-Agent Shepherding via Reinforcement Learningcited by 2
- 2025Hierarchical Learning-Based Control for Multi-Agent Shepherding of Stochastic Autonomous Agentscited by 2
- 2024Robotic Shepherding in Cluttered and Unknown Environments using Control Barrier Functionscited by 1
- 2025Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targetscited by 1
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- 2026Task-Oriented Formation Decision via Reinforcement Learning: Herding an Attacking Swarm
- 2026Sensor to Pixels: Decentralized Swarm Gathering via Image-Based Reinforcement Learning
- 2024A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation
- 2025Sparse shepherding control of large-scale multi-agent systems via Reinforcement Learning
- 2024Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertainty
- 2025Learning Efficient Flocking Control based on Gibbs Random Fields
- 2026Modular Reinforcement Learning For Cooperative Swarms
- 2024Distributed Autonomous Swarm Formation for Dynamic Network Bridging