Work

LEO-GYM: A Reinforcement Learning Library for Satellite Control in LEO

Year: 2025

Type: article

Abstract: Motivated by recent advances in Reinforcement Learning (RL) and the lack of open-source tools for training and benchmarking satellite guidance and control, we introduce LEO-GYM: a lightweight Python library for formulating RL problems for satellites in Low Earth Orbit (LEO). The framework decomposes problems into three classes, the low-level dynamics, the training environment and a satellite object that bridges them. LEO-GYM enables the creation of custom scenarios without imposing rigid class hierarchies. We present the architecture, key components, and an illustrative orbit-correction task modeled as a semi-Markov decision process. LEO-GYM is released as open-source to support and foster reproducible research in autonomous space operations.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2026.01.073

Authors: Nektarios Aristeidis Tafanidis, Avijit Banerjee, George Nikolakopoulos

Institution: Luleå University of Technology

Language: English

Topic: Spacecraft Dynamics and Control

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: European Space Agency

Award: G8812976473