Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
This addresses the challenge of designing interpretable and rapid AI interventions for behavior change in frictionful tasks, which is an incremental advancement in human-AI interaction.
The paper tackles the problem of AI agents providing personalized interventions to help individuals stick to long-term, effortful goals by introducing Behavior Model Reinforcement Learning (BMRL), a framework where the AI intervenes on the parameters of a Markov Decision Process belonging to a boundedly rational human agent, and shows theoretically and empirically that this approach leads to helpful policies across a range of complex human models.
Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification. Here, an artificial intelligence (AI) agent can provide personalized interventions to help individuals stick to their goals. In these settings, the AI agent must personalize rapidly (before the individual disengages) and interpretably, to help us understand the behavioral interventions. In this paper, we introduce Behavior Model Reinforcement Learning (BMRL), a framework in which an AI agent intervenes on the parameters of a Markov Decision Process (MDP) belonging to a boundedly rational human agent. Our formulation of the human decision-maker as a planning agent allows us to attribute undesirable human policies (ones that do not lead to the goal) to their maladapted MDP parameters, such as an extremely low discount factor. Furthermore, we propose a class of tractable human models that captures fundamental behaviors in frictionful tasks. Introducing a notion of MDP equivalence specific to BMRL, we theoretically and empirically show that AI planning with our human models can lead to helpful policies on a wide range of more complex, ground-truth humans.