mlsanityData to 14 Oct 2026

Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks

arXiv 2601.08254 ↗First posted 13 Jan 2026cs.AI cs.SY eess.SYcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Large AI Model (LAM) have been proposed to applications of Non-Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific trainings. In this paper, we propose a Deep Reinforcement Learning (DRL) agent that is guided by a Large Language Model (LLM). The LLM operates as a high level coordinator that generates textual guidance that shape the reward of the DRL agent during training. The results show that the LAM-DRL outperforms the traditional DRL by 40% in nominal weather scenarios and 64% in extreme weather scenarios compared to heuristics in terms of throughput, fairness, and outage probability.

Key facts. Cited by 1 paper 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 4 of its 15 references that are in the map. First posted on arXiv 13 Jan 2026.

Builds on

4 of 15 references.

References · 4

  1. 2020Satellite Communications in the New Space Era: A Survey and Future Challengescited by 36
  2. 2024Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Casescited by 12
  3. 2021Evolution of Non-Terrestrial Networks From 5G to 6G: A Surveycited by 9
  4. 2021NB-IoT via LEO satellites: An efficient resource allocation strategy for uplink data transmissioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
  2. 2024Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
  3. 2023Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
  4. 2024Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings
  5. 2024To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
  6. 2025Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
  7. 2023Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
  8. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey