Exploring the Potential of Reinforcement Learning for Applications in Hydrogen Energy Systems

The frequent occurrence of system failures in hydrogen energy is a significant impediment to its development and widespread adoption. Currently, the field of hydrogen energy safety monitoring is rapidly advancing, yet few researchers have proposed appropriate solutions to address system failures. This challenge arises from the complex and interconnected nature of hydrogen energy systems. Furthermore, unlike other energy sources, hydrogen is highly combustible and explosive, and susceptible to leaks, necessitating the consideration of multiple factors. In this context, reinforcement learning, a machine learning control technology renowned for its robust decision-making abilities, has garnered considerable interest among researchers. To date, however, reinforcement learning has not been applied in the field of hydrogen safety. This paper provides a concise introduction to several commonly used reinforcement learning algorithms, analyzes the various fault types and fault diagnosis modes of hydrogen energy, and highlights the adequacy and necessity of reinforcement learning in the field of hydrogen energy. Furthermore, this paper outlines a new research direction that can pave the way for future developments in hydrogen safety.