Personalizing Dialog Policy in Health Guidance via Model-based Multitask Reinforcement Learning

Taiga Sano, Masahiro Kohjima, Masami Takahashi, Kaori Fujimura, Tae Sato, Yurika Katagiri

Abstract: Personalized interventions through dialog-based health guidance effectively encourage healthy behaviors for clients (i.e., individuals) at risk of lifestyle diseases. However, it is not obvious which dialog policies would enable the best understanding of the client’s characteristics and subsequent provision of tailored interventions. To meet this need, we propose a dialog policy learning method for both supporting professionals and directly motivating clients via chatbots. We model the dialog to identify tailored interventions, considering the heterogeneity among clients, by multitask reinforcement learning and train an agent in a model-based manner utilizing real-world data and reward shaping to ensure the safety of early agents’ statements. A simulation study utilizing real data shows that our approach achieves a success rate of 76% in identifying tailored interventions with few questions, outperforming standard methods, including large language models, which had a 42% success rate.