Model-based deep reinforcement learning with heuristic search for satellite attitude control

Purpose: Recently, deep reinforcement learning is developing rapidly and shows its power to solve difficult problems such as robotics and game of GO. Meanwhile, satellite attitude control systems are still using classical control technics such as proportional – integral – derivative and slide mode control as major solutions, facing problems with adaptability and automation. Design/methodology/approach: In this paper, an approach based on deep reinforcement learning is proposed to increase adaptability and autonomy of satellite control system. It is a model-based algorithm which could find solutions with fewer episodes of learning than model-free algorithms. Findings: Simulation experiment shows that when classical control crashed, this approach could find solution and reach the target with hundreds times of explorations and learning. Originality/value: This approach is a non-gradient method using heuristic search to optimize policy to avoid local optima. Compared with classical control technics, this approach does not need prior knowledge of satellite or its orbit, has the ability to adapt different kinds of situations with data learning and has the ability to adapt different kinds of satellite and different tasks through transfer learning.