# Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrays

arXiv: https://arxiv.org/abs/2604.05162 · HTML: https://mlsanity.com/paper/2604.05162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.11318/) (2024)
- [Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications](https://mlsanity.com/paper/1912.07990/) (2019)
- [An Overview of Signal Processing Techniques for RIS/IRS-aided Wireless Systems](https://mlsanity.com/paper/2112.05989/) (2021)
- [Coverage Enhancement for NLOS mmWave Links Using Passive Reflectors](https://mlsanity.com/paper/1905.04794/) (2019)
- [Reconfigurable Intelligent Surfaces: A Signal Processing Perspective With Wireless Applications](https://mlsanity.com/paper/2102.00742/) (2021)
- [A Deep Reinforcement Learning Approach for Autonomous Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2403.09270/) (2024)
- [Insight Gained from Migrating a Machine Learning Model to Intelligence Processing Units](https://mlsanity.com/paper/2404.10730/) (2024)
- [Guiding Wireless Signals with Arrays of Metallic Linear Fresnel Reflectors: A Low-cost, Frequency-versatile, and Practical Approach](https://mlsanity.com/paper/2407.19179/) (2024)
- [Signal Whisperers: Enhancing Wireless Reception Using DRL-Guided Reflector Arrays](https://mlsanity.com/paper/2501.15044/) (2025)
- [Practical Channel Estimation and Phase Shift Design for Intelligent Reflecting Surface Empowered MIMO Systems](https://mlsanity.com/paper/2104.14161/) (2021)

## Abstract (arXiv)

Reconfigurable Intelligent Surfaces (RIS) are pivotal for next-generation smart radio environments, yet their practical deployment is severely bottlenecked by the intractable computational overhead of Channel State Information (CSI) estimation. To bypass this fundamental physical-layer barrier, we propose an AI-native, data-driven paradigm that replaces complex channel modeling with spatial intelligence. This paper presents a fully autonomous Multi-Agent Reinforcement Learning (MARL) framework to control mechanically adjustable metallic reflector arrays. By mapping high-dimensional mechanical constraints to a reduced-order virtual focal point space, we deploy a Centralized Training with Decentralized Execution (CTDE) architecture. Using Multi-Agent Proximal Policy Optimization (MAPPO), our decentralized agents learn cooperative beam-focusing strategies relying on user coordinates, achieving CSI-free operation. High-fidelity ray-tracing simulations in dynamic non-line-of-sight (NLOS) environments demonstrate that this multi-agent approach rapidly adapts to user mobility, yielding up to a 26.86 dB enhancement over static flat reflectors and outperforming single-agent and hardware-constrained DRL baselines in both spatial selectivity and temporal stability. Crucially, the learned policies exhibit good deployment resilience, sustaining stable signal coverage even under 1.0-meter localization noise. These results validate the efficacy of MARL-driven spatial abstractions as a scalable, highly practical pathway toward AI-empowered wireless networks.