UC Berkeley

Reinforcement learning (RL) has been widely used to study and model human, animal and artificial intelligence. In this thesis, we focus on modeling human learning and decision making, and exemplify two ways mathematical RL modeling can add to our existing knowledge of human cognition: (1) provide a quantitative tool for parametrizing individual differences in human behavior, and (2) augment existing models to account for complex multi-step human learning.