  Systems and machines undergo various failure modes that result in machine
health degradation, so maintenance actions are required to restore them back to
a state where they can perform their expected functions. Since maintenance
tasks are inevitable, maintenance planning is essential to ensure the smooth
operations of the production system and other industries at large. Maintenance
planning is a decision-making problem that aims at developing optimum
maintenance policies and plans that help reduces maintenance costs, extend
asset life, maximize their availability, and ultimately ensure workplace
safety. Reinforcement learning is a data-driven decision-making algorithm that
has been increasingly applied to develop dynamic maintenance plans while
leveraging the continuous information from condition monitoring of the system
and machine states. By leveraging the condition monitoring data of systems and
machines with reinforcement learning, smart maintenance planners can be
developed, which is a precursor to achieving a smart factory. This paper
presents a literature review on the applications of reinforcement and deep
reinforcement learning for maintenance planning and optimization problems. To
capture the common ideas without losing touch with the uniqueness of each
publication, taxonomies used to categorize the systems were developed, and
reviewed publications were highlighted, classified, and summarized based on
these taxonomies. Adopted methodologies, findings, and well-defined
interpretations of the reviewed studies were summarized in graphical and
tabular representations to maximize the utility of the work for both
researchers and practitioners. This work also highlights the research gaps, key
insights from the literature, and areas for future work.
