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Nowadays, realistic simulation environments are essential to validate andbuild reliable robotic solutions. This is particularly true when usingReinforcement Learning (RL) based control policies. To this end, both roboticsand RL developers need tools and workflows to create physically accuratesimulations and synthetic datasets. Gazebo, MuJoCo, Webots, Pybullets or IsaacSym are some of the many tools available to simulate robotic systems.Developing learning-based methods for space navigation is, due to the highlycomplex nature of the problem, an intensive data-driven process that requireshighly parallelized simulations. When it comes to the control of spacecrafts,there is no easy to use simulation library designed for RL. We address this gapby harnessing the capabilities of NVIDIA Isaac Gym, where both physicssimulation and the policy training reside on GPU. Building on this tool, weprovide an open-source library enabling users to simulate thousands of parallelspacecrafts, that learn a set of maneuvering tasks, such as position, attitude,and velocity control. These tasks enable to validate complex space scenarios,such as trajectory optimization for landing, docking, rendezvous and more.

RANS: Highly-Parallelised Simulator for Reinforcement Learning based Autonomous Navigating Spacecrafts