Deep reinforcement learning method based on DDPG with simulated annealing for satellite attitude control system

Satellite attitude control has been a hot issue in recent years. The rapid development of deep reinforcement learning provides a new choice for the solution of traditional control problems. A deep reinforcement learning algorithm based on DDPG for satellite attitude control system has been proposed by this paper. The method uses a simulated annealing algorithm to seek out the global optimal policy. This method can achieve robust effect in a shorter period of time compared with other algorithms through experiments.