# Close-Proximity Satellite Operations through Deep Reinforcement Learning and Terrestrial Testing Environments

arXiv: https://arxiv.org/abs/2502.20554 · HTML: https://mlsanity.com/paper/2502.20554/

## 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 10 of its 27 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## 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, 10 of 10)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Action Robust Reinforcement Learning and Applications in Continuous Control](https://mlsanity.com/paper/1901.09184/) (2019)
- [Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective](https://mlsanity.com/paper/2009.03728/) (2020)
- [Scaling Up Robust MDPs by Reinforcement Learning](https://mlsanity.com/paper/1306.6189/) (2013)
- [Supervision via Competition: Robot Adversaries for Learning Tasks](https://mlsanity.com/paper/1610.01685/) (2016)
- [Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination](https://mlsanity.com/paper/2308.02743/) (2023)
- [Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning](https://mlsanity.com/paper/2302.14188/) (2023)
- [Space Trusted Autonomy Readiness Levels](https://mlsanity.com/paper/2210.09059/) (2022)
- [Stability Analysis of Deep Reinforcement Learning for Multi-Agent Inspection in a Terrestrial Testbed](https://mlsanity.com/paper/2503.00056/) (2025)

## Abstract (arXiv)

With the increasingly congested and contested space environment, safe and effective satellite operation has become increasingly challenging. As a result, there is growing interest in autonomous satellite capabilities, with common machine learning techniques gaining attention for their potential to address complex decision-making in the space domain. However, the "black-box" nature of many of these methods results in difficulty understanding the model's input/output relationship and more specifically its sensitivity to environmental disturbances, sensor noise, and control intervention. This paper explores the use of Deep Reinforcement Learning (DRL) for satellite control in multi-agent inspection tasks. The Local Intelligent Network of Collaborative Satellites (LINCS) Lab is used to test the performance of these control algorithms across different environments, from simulations to real-world quadrotor UAV hardware, with a particular focus on understanding their behavior and potential degradation in performance when deployed beyond the training environment.