Research Objective

This research aims to integrate human cognitive biases (such as loss aversion and probability weighting via Prospect Theory) into the reward modeling of Reinforcement Learning (RL) agents. By evaluating agents in environments with high uncertainty like the Iowa Gambling Task (IGT), we aim to simulate realistic human decision-making and develop AI systems that are better aligned with human values and risk preferences.