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.
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 , as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.