Public Goods Game Simulator with Reinforcement Learning Agents

Abstract

As a famous game in the domain of game theory, both pervasive empirical studies as well as intensive theoretical analysis have been conducted and performed worldwide to research different public goods game scenarios. At the same time, computer game simulators are utilized widely for better research of game theory by providing easy but powerful visualization and statistics functionalities. However, although solutions of public goods game have been widely discussed with empirical studies or theoretical approaches, no computational and automatic simulation approaches has been adopted. For this reason, we have implemented a computer simulator with reinforcement learning agents module for public goods game, and we have utilized this simulator to further study the characteristics of public goods game. Furthermore, in this article, we have also presented a bunch of interesting experimental results with respect to the strategies that agents used and the profits they earned.