# Reward-Reinforced Reinforcement Learning for Multi-agent Systems

arXiv: https://arxiv.org/abs/2103.12192 · HTML: https://mlsanity.com/paper/2103.12192/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces](https://mlsanity.com/paper/2107.12706/) (2021)
- [A Framework for History-Aware Hyperparameter Optimisation in Reinforcement Learning](https://mlsanity.com/paper/2303.05186/) (2023)
- [FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game](https://mlsanity.com/paper/2402.00738/) (2024)
- [3D-Agent:Tri-Modal Multi-Agent Collaboration for Scalable 3D Object Annotation](https://mlsanity.com/paper/2601.04404/) (2026)

## Abstract (arXiv)

Reinforcement learning algorithms in multi-agent systems deliver highly resilient and adaptable solutions for common problems in telecommunications,aerospace, and industrial robotics. However, achieving an optimal global goal remains a persistent obstacle for collaborative multi-agent systems, where learning affects the behaviour of more than one agent. A number of nonlinear function approximation methods have been proposed for solving the Bellman equation, which describe a recursive format of an optimal policy. However, how to leverage the value distribution based on reinforcement learning, and how to improve the efficiency and efficacy of such systems remain a challenge. In this work, we developed a reward-reinforced generative adversarial network to represent the distribution of the value function, replacing the approximation of Bellman updates. We demonstrated our method is resilient and outperforms other conventional reinforcement learning methods. This method is also applied to a practical case study: maximising the number of user connections to autonomous airborne base stations in a mobile communication network. Our method maximises the data likelihood using a cost function under which agents have optimal learned behaviours. This reward-reinforced generative adversarial network can be used as ageneric framework for multi-agent learning at the system level