# Aerial Multi-Functional RIS in Fluid Antennas-Aided Full-Duplex Networks: A Self-Optimized Hybrid Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2604.14309 · HTML: https://mlsanity.com/paper/2604.14309/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Five Facets of 6G: Research Challenges and Opportunities](https://mlsanity.com/paper/2212.07902/) (2022)
- [Fluid Antenna Systems](https://mlsanity.com/paper/2005.11561/) (2020)
- [Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems](https://mlsanity.com/paper/2110.04859/) (2021)
- [Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer](https://mlsanity.com/paper/2205.05029/) (2022)
- [Simultaneously Transmitting and Reflecting Surface (STARS) for Terahertz Communications](https://mlsanity.com/paper/2212.00497/) (2022)
- [D-STAR: Dual Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces for Joint Uplink/Downlink Transmission](https://mlsanity.com/paper/2307.16096/) (2023)
- [AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities](https://mlsanity.com/paper/2311.04241/) (2023)
- [Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-based Multi-Agent Proximal Policy Optimization DRL with Attention Mechanism](https://mlsanity.com/paper/2209.07228/) (2022)
- [Meta Reinforcement Learning for Resource Allocation in Aerial Active-RIS-assisted Networks with Rate-Splitting Multiple Access](https://mlsanity.com/paper/2403.08648/) (2024)
- [RIS-Aided Fluid Antenna Array-Mounted UAV Networks](https://mlsanity.com/paper/2501.09351/) (2025)
- [Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks](https://mlsanity.com/paper/2501.11079/) (2025)
- [Outage Probability of UAV Communications in the Presence of Interference](https://mlsanity.com/paper/1806.09843/) (2018)
- [Downlink Rate Maximization with Reconfigurable Intelligent Surface Assisted Full-Duplex Transmissions](https://mlsanity.com/paper/2311.03579/) (2023)
- [Energy-aware Trajectory Optimization for UAV-mounted RIS and Full-duplex Relay](https://mlsanity.com/paper/2401.12107/) (2024)
- [Multi-Functional RIS-Enabled in SAGIN for IoT: A Hybrid Deep Reinforcement Learning Approach with Compressed Twin-Models](https://mlsanity.com/paper/2507.16204/) (2025)
- [Joint Active and Passive Beamforming for Energy-Efficient STARS with Quantization and Element Selection in ISAC Systems](https://mlsanity.com/paper/2507.16210/) (2025)
- [Fluid Antenna Array Enhanced Over-the-Air Computation](https://mlsanity.com/paper/2312.15244/) (2023)
- [Movable Antenna-Enabled Co-Frequency Co-Time Full-Duplex Wireless Communication](https://mlsanity.com/paper/2401.17049/) (2024)
- [Movable Antenna-Aided Secure Full-Duplex Multi-User Communications](https://mlsanity.com/paper/2407.10393/) (2024)
- [Fluid Antennas: Reshaping Intrinsic Properties for Flexible Radiation Characteristics in Intelligent Wireless Networks](https://mlsanity.com/paper/2501.02911/) (2025)
- [Liquid Intelligent Metasurface for Fluid Antennas-Assisted Networks](https://mlsanity.com/paper/2507.16211/) (2025)
- [Parametrized Sharing for Multi-Agent Hybrid DRL for Multiple Multi-Functional RISs-Aided Downlink NOMA Networks](https://mlsanity.com/paper/2601.00538/) (2026)

## Abstract (arXiv)

To address high data traffic demands of sixth-generation (6G) networks, this paper proposes a novel architecture that integrates autonomous aerial vehicles (AAVs) and multi-functional reconfigurable intelligent surfaces (MF-RISs) as AM-RIS in fluid antenna (FA)-assisted full-duplex (FD) networks. The AM-RIS provides hybrid functionalities, including signal reflection, amplification, and energy harvesting (EH), potentially improving both signal coverage and sustainability. Meanwhile, FA facilitates fine-grained spatial adaptability at FD-enabled base station (BS), which complements residual self-interference (SI) suppression. We aim at maximizing the overall energy efficiency (EE) by jointly optimizing transmit DL beamforming at BS, UL user power, configuration of AM-RIS, and positions of the FA and AM-RIS. Owing to the hybrid continuous-discrete parameters and high dimensionality of the intractable problem, we have conceived a self-optimized multi-agent hybrid deep reinforcement learning (DRL) framework (SOHRL), which integrates multi-agent deep Q-networks (DQN) and multi-agent proximal policy optimization (PPO), respectively handling discrete and continuous actions. To enhance self-adaptability, an attention-driven state representation and meta-level hyperparameter optimization are incorporated, enabling multi-agents to autonomously adjust learning hyperparameters. Simulation results validate the effectiveness of the proposed AM-RIS-enabled FA-aided FD networks empowered by SOHRL algorithm. The results reveal that SOHRL outperforms benchmarks of the case without attention mechanism and conventional hybrid/multi-agent/standalone DRL. Moreover, AM-RIS in FD achieves the highest EE compared to half-duplex, conventional rigid antenna arrays, partial EH, and conventional RIS without amplification, highlighting its potential as a compelling solution for EE-aware wireless networks.