# Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems

arXiv: https://arxiv.org/abs/2501.06554 · HTML: https://mlsanity.com/paper/2501.06554/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 6 of 6)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [Option-Critic in Cooperative Multi-agent Systems](https://mlsanity.com/paper/1911.12825/) (2019)
- [Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets](https://mlsanity.com/paper/2203.03684/) (2022)
- [Attention Option-Critic](https://mlsanity.com/paper/2201.02628/) (2022)
- [Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2103.15371/) (2021)

## Abstract (arXiv)

This paper presents a hierarchical reinforcement learning (RL) approach to address the agent grouping or pairing problem in cooperative multi-agent systems. The goal is to simultaneously learn the optimal grouping and agent policy. By employing a hierarchical RL framework, we distinguish between high-level decisions of grouping and low-level agents' actions. Our approach utilizes the CTDE (Centralized Training with Decentralized Execution) paradigm, ensuring efficient learning and scalable execution. We incorporate permutation-invariant neural networks to handle the homogeneity and cooperation among agents, enabling effective coordination. The option-critic algorithm is adapted to manage the hierarchical decision-making process, allowing for dynamic and optimal policy adjustments.