# Reinforcement Learning for Opportunistic Routing in Software-Defined LEO-Terrestrial Systems

arXiv: https://arxiv.org/abs/2601.13662 · HTML: https://mlsanity.com/paper/2601.13662/

## Key facts

- Cited by 1 papers in the map (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 4 of its 19 references that are in the map.
- First posted on arXiv 20 Jan 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, 4 of 4)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Residual Policy Learning](https://mlsanity.com/paper/1812.06298/) (2018)
- [A Tutorial on Non-Terrestrial Networks: Towards Global and Ubiquitous 6G Connectivity](https://mlsanity.com/paper/2412.16611/) (2024)
- [A Scalable Architecture for Future Regenerative Satellite Payloads](https://mlsanity.com/paper/2407.06075/) (2024)

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

- [Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach](https://mlsanity.com/paper/2604.27478/) (2026)

## Abstract (arXiv)

The proliferation of large-scale low Earth orbit (LEO) satellite constellations is driving the need for intelligent routing strategies that can effectively deliver data to terrestrial networks under rapidly time-varying topologies and intermittent gateway visibility. Leveraging the global control capabilities of a geostationary (GEO)-resident software-defined networking (SDN) controller, we introduce opportunistic routing, which aims to minimize delivery delay by forwarding packets to any currently available ground gateways rather than fixed destinations. This makes it a promising approach for achieving low-latency and robust data delivery in highly dynamic LEO networks. Specifically, we formulate a constrained stochastic optimization problem and employ a residual reinforcement learning framework to optimize opportunistic routing for reducing transmission delay. Simulation results over multiple days of orbital data demonstrate that our method achieves significant improvements in queue length reduction compared to classical backpressure and other well-known queueing algorithms.