# Decentralized Shepherding of Non-Cohesive Swarms Through Cluttered Environments via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2511.21405 · HTML: https://mlsanity.com/paper/2511.21405/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach](https://mlsanity.com/paper/1912.07796/) (2019)
- [Invisible control of self-organizing agents leaving unknown environments](https://mlsanity.com/paper/1504.04064/) (2015)
- [Herding stochastic autonomous agents via local control rules and online global target selection strategies](https://mlsanity.com/paper/2010.00386/) (2020)
- [Learning to Herd Agents Amongst Obstacles: Training Robust Shepherding Behaviors using Deep Reinforcement Learning](https://mlsanity.com/paper/2005.09476/) (2020)
- [Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environments](https://mlsanity.com/paper/2301.10363/) (2023)
- [Emergent Cooperative Strategies for Multi-Agent Shepherding via Reinforcement Learning](https://mlsanity.com/paper/2411.05454/) (2024)
- [Hierarchical Learning-Based Control for Multi-Agent Shepherding of Stochastic Autonomous Agents](https://mlsanity.com/paper/2508.02632/) (2025)
- [Robotic Shepherding in Cluttered and Unknown Environments using Control Barrier Functions](https://mlsanity.com/paper/2407.15701/) (2024)
- [Hierarchical Policy-Gradient Reinforcement Learning for Multi-Agent Shepherding Control of Non-Cohesive Targets](https://mlsanity.com/paper/2504.02479/) (2025)

## 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.