Metz, YannickSchlegel, UdoSeebacher, DanielEl-Assady, MennatallahKeim, DanielBernard, JürgenAngelini, Marco2022-06-022022-06-022022978-3-03868-183-02664-4487https://doi.org/10.2312/eurova.20221074https://diglib.eg.org:443/handle/10.2312/eurova20221074Multiple challenges hinder the application of reinforcement learning algorithms in experimental and real-world use cases even with recent successes in such areas. Such challenges occur at different stages of the development and deployment of such models. While reinforcement learning workflows share similarities with machine learning approaches, we argue that distinct challenges can be tackled and overcome using visual analytic concepts. Thus, we propose a comprehensive workflow for reinforcement learning and present an implementation of our workflow incorporating visual analytic concepts integrating tailored views and visualizations for different stages and tasks of the workflow.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Reinforcement learningHuman centered computingVisual analyticsComputing methodologiesReinforcement learning10.2312/eurova.2022107419-235 pages