# Auction-Based RIS Allocation With DRL: Controlling the Cost-Performance Trade-Off

arXiv: https://arxiv.org/abs/2603.04433 · HTML: https://mlsanity.com/paper/2603.04433/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 18 Feb 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, 5 of 5)

- [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)
- [Energy Efficiency Maximization in RIS-Aided Cell-Free Network with Limited Backhaul](https://mlsanity.com/paper/2012.11736/) (2020)
- [System Performance Insights into Design of RIS-assisted Smart Radio Environments for 6G](https://mlsanity.com/paper/2101.01102/) (2021)

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

- [Cooperative Deep Reinforcement Learning for Fair RIS Allocation](https://mlsanity.com/paper/2603.25572/) (2026)

## Abstract (arXiv)

We study the allocation of reconfigurable intelligent surfaces (RISs) in a multi-cell wireless network, where base stations compete for control of shared RIS units deployed at the cell edges. These RISs, provided by an independent operator, are dynamically leased to the highest bidder using a simultaneously ascending auction format. Each base station estimates the utility of acquiring additional RISs based on macroscopic channel parameters, enabling a scalable and low-overhead allocation mechanism. To optimize the bidding behavior, we integrate deep reinforcement learning (DRL) agents that learn to maximize performance while adhering to budget constraints. Through simulations in clustered cell-edge environments, we demonstrate that reinforcement learning (RL)-based bidding significantly outperforms heuristic strategies, achieving optimal trade-offs between cost and spectral efficiency. Furthermore, we introduce a tunable parameter that governs the bidding aggressiveness of RL agents, enabling a flexible control of the trade-off between network performance and expenditure. Our results highlight the potential of combining auction-based allocation with adaptive RL mechanisms for efficient and fair utilization of RISs in next-generation wireless networks.