Fitting Reinforcement Learning Model to Behavioral Data under Bandits

ArXi:2511.04454v2 Announce Type: replace-cross We consider the problem of fitting a reinforcement learning (RL) model to some given behavioral data under a multi-armed bandit environment. These models have received much attention in recent years for characterizing human and animal decision making behavior. We provide a generic mathematical optimization problem formulation for the fitting problem of a wide range of RL models that appear frequently in scientific research applications. We then provide a detailed theoretical analysis of its convexity properties.