# Cooperative Deep Reinforcement Learning for Fair RIS Allocation

arXiv: https://arxiv.org/abs/2603.25572 · HTML: https://mlsanity.com/paper/2603.25572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [SuperSuit: Simple Microwrappers for Reinforcement Learning Environments](https://mlsanity.com/paper/2008.08932/) (2020)
- [User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions](https://mlsanity.com/paper/2104.14589/) (2021)
- [Energy Efficiency Maximization in RIS-Aided Cell-Free Network with Limited Backhaul](https://mlsanity.com/paper/2012.11736/) (2020)
- [A Hierarchical DRL Approach for Resource Optimization in Multi-RIS Multi-Operator Networks](https://mlsanity.com/paper/2410.12320/) (2024)
- [Auction-Based RIS Allocation With DRL: Controlling the Cost-Performance Trade-Off](https://mlsanity.com/paper/2603.04433/) (2026)

## Abstract (arXiv)

The deployment of reconfigurable intelligent surfaces (RISs) introduces new challenges for resource allocation in multi-cell wireless networks, particularly when user loads are uneven across base stations. In this work, we consider RISs as shared infrastructure that must be dynamically assigned among competing base stations, and we address this problem using a simultaneous ascending auction mechanism. To mitigate performance imbalances between cells, we propose a fairness-aware collaborative multi-agent reinforcement learning approach in which base stations adapt their bidding strategies based on both expected utility gains and relative service quality. A centrally computed performance-dependent fairness indicator is incorporated into the agents' observations, enabling implicit coordination without direct inter-base-station communication. Simulation results show that the proposed framework effectively redistributes RIS resources toward weaker-performing cells, substantially improving the rates of the worst-served users while preserving overall throughput. The results demonstrate that fairness-oriented RIS allocation can be achieved through cooperative learning, providing a flexible tool for balancing efficiency and equity in future wireless networks.