Application of Deep Reinforcement Learning to Thermal Control of Space Telescope

Abstract

With the development of deep space exploration technology, thermal control systems for space telescopes are becoming increasingly complex, leading to the key parameters of conventional thermal control systems are difficult to adjust online automatically. To achieve these adjustments, this paper provided detailed verification of the application of deep reinforcement learning to space telescope thermal control from three perspectives.

Yan XIONG(熊琰)

Ph.D. student from the University of Chinese Academy of Sciences

My research interests include intelligent control of gyroscopes using model-based reinforcement learning, intelligent thermal control based on deep reinforcement learning for space load, and engineering applications of AI, such as robotic systems, spacecraft strategic planning, etc.