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

arXiv: https://arxiv.org/abs/2601.08254 · HTML: https://mlsanity.com/paper/2601.08254/

## 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 4 of its 15 references that are in the map.
- First posted on arXiv 13 Jan 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, 4 of 4)

- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases](https://mlsanity.com/paper/2405.20568/) (2024)
- [Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey](https://mlsanity.com/paper/2107.06881/) (2021)
- [NB-IoT via LEO satellites: An efficient resource allocation strategy for uplink data transmission](https://mlsanity.com/paper/2107.01067/) (2021)

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

- [When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks](https://mlsanity.com/paper/2604.03774/) (2026)

## 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.