Digital Therapeutics (DTx) are typically considered as patient-facing software applications delivering behavior change interventions to treat non-communicable diseases (e.g., cardiovascular diseases, obesity, diabetes). In recent years, they have successfully developed into a new pillar of care. A central promise of DTx is the idea of personalizing medical interventions to the needs and characteristics of the patient. The present literature review sheds light on using reinforcement learning, a subarea of machine learning, for personalizing DTxdelivered care pathways via self-learning software agents. Based on the analysis of 36 studies, the paper reviews the state of the art regarding the used algorithms, the objects of personalization, evaluation methods, and metrics. In sum, the results highlight the potential and could already demonstrate the medical efficacy. Implications for practice and future research are derived and discussed in order to bring self-learning DTx applications one step closer to everyday care.