{"ID":2842280,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13746","arxiv_id":"2511.13746","title":"Deep reinforcement learning-based spacecraft attitude control with pointing keep-out constraint","abstract":"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.","short_abstract":"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 at...","url_abs":"https://arxiv.org/abs/2511.13746","url_pdf":"https://arxiv.org/pdf/2511.13746v1","authors":"[\"Juntang Yang\",\"Mohamed Khalil Ben-Larbi\"]","published":"2025-11-13T13:45:22Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}