mlsanityData to 7 Oct 2026

Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning

arXiv 2409.18718 ↗First posted 27 Sep 2024cs.NI cs.LGcited by 2

On the map: Offline RL and imitation learning from data → Offline multi-agent RL

Abstract (arXiv)

In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association in NTNs. Traditional reinforcement learning (RL) methods for wireless network optimization often rely on manually designed reward functions, which can require extensive parameter tuning. To overcome these limitations, we employ inverse RL (IRL), specifically leveraging the GAIL framework, to automatically learn reward functions without manual design. We augment this framework with an asynchronous federated learning approach, enabling decentralized multi-satellite systems to collaboratively derive optimal policies. The proposed method aims to maximize spectrum efficiency (SE) while meeting minimum information rate requirements for RUEs. To address the non-convex, NP-hard nature of this problem, we combine the many-to-one matching theory with a multi-agent asynchronous federated IRL (MA-AFIRL) framework. This allows agents to learn through asynchronous environmental interactions, improving training efficiency and scalability. The expert policy is generated using the Whale optimization algorithm (WOA), providing data to train the automatic reward function within GAIL. Simulation results show that the proposed MA-AFIRL method outperforms traditional RL approaches, achieving a 14.6% improvement in convergence and reward value. The novel GAIL-driven policy learning establishes a novel benchmark for 6G NTN optimization.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Offline multi-agent RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 49 references that are in the map. First posted on arXiv 27 Sep 2024.

Builds on

14 of 49 references.

References · 14

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
  3. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  4. 2024Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6Gcited by 30
  5. 2019Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communicationcited by 21
  6. 2023Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6Gcited by 12
  7. 2023Inverse Reinforcement Learning with the Average Reward Criterioncited by 8
  8. 2020Massive MIMO Transmission for LEO Satellite Communicationscited by 7
  9. 2020Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networkscited by 7
  10. 2021Matching Algorithms: Fundamentals, Applications and Challengescited by 7
  11. 2022Satellite-based ITS Data Offloading & Computation in 6G Networks: A Cooperative Multi-Agent Proximal Policy Optimization DRL with Attention Approachcited by 4
  12. 2021EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehiclescited by 3
2 more
  1. 2022Seamless and Energy Efficient Maritime Coverage in Coordinated 6G Space-Air-Sea Non-Terrestrial Networkscited by 3
  2. 2023SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimizationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
  2. 2026Can Optimal Transport Improve Federated Inverse Reinforcement Learning?cited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
  2. 2024An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
  3. 2023Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
  4. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions
  5. 2026Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization
  6. 2025Generative Resource Allocation for 6G O-RAN with Diffusion Policies
  7. 2023Communication Load Balancing via Efficient Inverse Reinforcement Learning
  8. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models