Deep reinforcement learning-based spacecraft attitude control with pointing keep-out constraint
This addresses attitude control for spacecraft with specific pointing constraints, but it is incremental as it applies existing DRL methods to a new domain.
The paper tackled spacecraft reorientation control with a pointing keep-out constraint using deep reinforcement learning, achieving effective control as demonstrated in simulations.
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.