Before revision:  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 . Empirically, the proposed model outperforms Q-Learning and  Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks. 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 long-term recommendation systems .
After revision:  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 . Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributionsand real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities .