Text summarization, question answering, machine translation, and predictive text are all NLP applications using reinforcement learning. This includes tutoring systems that adapt to student needs, identify knowledge gaps, and suggest customized learning trajectories to enhance educational outcomes. Reinforcement learning can be used to create personalized learning experiences for students. The outputs are the treatment options or drug dosages for every stage of the patient’s journey.
The input is a set of clinical observations and assessments of a patient.
Reinforcement learning can be used to create personalized treatment strategies, known as dynamic treatment regimes (DTRs), for patients with long-term illnesses. Some real-life applications of reinforcement learning include:
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