Multi-addicted State Q-agent Decision Making

In nature, there may exist common characteristics in information processing and decision making that are exhibited by different type of systems. It has long been proposed that reinforcement learning algorithms and the neural mechanism of human decision making are highly alike. In this study, we investigated different Q-agents with different exploration strategies decision-making performance under a discrete multi-addicted state environment. The below figure demonstates the environmental setup of this study.

You can see the manuscript for this study at here.