Abstract
Drawing an inspiration from behavioral studies
of human decision making, we propose here a
general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a twostream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including
Parkinson’s and Alzheimer’s diseases, attentiondeficit/hyperactivity disorder (ADHD), addiction,
and chronic pain. For AI community, the development of agents that react differently to different types of rewards can enable us to understand a
wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Moreover, from the behavioral modeling perspective, our
parametric framework can be viewed as a first step
towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in longterm recommendation systems.