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

arXiv: https://arxiv.org/abs/2504.02479 · HTML: https://mlsanity.com/paper/2504.02479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Emergence of Locomotion Behaviours in Rich Environments](https://mlsanity.com/paper/1707.02286/) (2017)
- [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)
- [Asymptotic behavior and control of a "guidance by repulsion" model](https://mlsanity.com/paper/1911.01133/) (2019)
- [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)
- [Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding](https://mlsanity.com/paper/2004.11543/) (2020)
- [Communication-Free Shepherding Navigation with Multiple Steering Agents](https://mlsanity.com/paper/2205.08155/) (2022)

## Built on by (most cited first, 1 of 1)

- [Decentralized Shepherding of Non-Cohesive Swarms Through Cluttered Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/2511.21405/) (2025)

## Abstract (arXiv)

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.