Before revision: This paper considers a novel formulation of inverse reinforcement learning ~ (IRL) with behavioral economics constraints to address inverse sequential hypothesis testing (SHT) and inverse search in Bayesian agents. We first estimate the stopping and search costs by observing the actions of these agents using Bayesian revealed preference from microeconomicsand rational inattention from behavioral economics.
After revision: This paper considers a novel formulation of inverse reinforcement learning ~ (IRL) framework for Bayesian stopping time problems. By observing the actions of these agents using Bayesian revealed preference from microeconomicsand rational inattention from behavioral economics.