Schema matching involves identifying matching relationships between different data
schemas. While this task is supported by semi-automatic tools, achieving optimal results requires
configuring such tools which can be challenging depending on the number of configuration options
and schema characteristics. This study proposes a novel approach utilizing Reinforcement Learning
(RL) to automate the configuration of schema matching tools. RL has proven to be well-suited for
complex optimization problems but has not yet been applied for schema matching. We outline how
the configuration of a schema matching tool can be tackled as an RL task and how the corresponding
learning process can be accelerated and optimized. We evaluate the RL approach for a large real-world
dataset and show that it can be applied to different matching tools.