Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks

TL;DR

This paper introduces a framework where AI personalizes interventions for boundedly rational humans in frictionful tasks by modeling their decision processes as MDPs, enabling effective and interpretable support strategies.

Contribution

The paper presents Behavior Model Reinforcement Learning (BMRL), a novel approach to personalize AI interventions by modeling human decision-making as MDPs and introducing tractable human behavior models.

Findings

AI planning with human models yields helpful policies across various human behaviors.

The framework can interpret human maladaptations as MDP parameter issues.

The approach is effective in complex, realistic frictionful tasks.

Abstract

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.