This paper implements deep reinforcement learning (DRL) for spacecraft reorientation control with a single pointing keep-out zone. The Soft Actor-Critic (SAC) algorithm is adopted to handle continuous state and action space. A new state representation is designed to explicitly include a compact representation of the attitude constraint zone. The reward function is formulated to achieve the control objective while enforcing the attitude constraint. A curriculum learning approach is used for the agent training. Simulation results demonstrate the effectiveness of the proposed DRL-based method for spacecraft pointing-constrained attitude control.
@article{arxiv.2511.13746,
title = {Deep reinforcement learning-based spacecraft attitude control with pointing keep-out constraint},
author = {Juntang Yang and Mohamed Khalil Ben-Larbi},
journal= {arXiv preprint arXiv:2511.13746},
year = {2025}
}