WemiEnv: An Open-Source Reinforcement Learning Platform for WeChat Mini-Games

Abstract

The popularity of mobile games has surged in recent years. Along with mobile games, the emergence of mini-games has recently raised attention. Compared to traditional mobile games, mini-games are more lightweight and platform-independent with low development cost, which has attracted thousands of developers and users. WeChat mini-games platform is one of the most popular platforms with over 100 000 mini-games. The diversity and variety of WeChat mini-games make it an ideal platform for training reinforcement learning (RL) agents. In contrast, most of the existing RL benchmark environments are equipped with predetermined games, which are always limited to several genres and lack the utilization of new and diverse games. To utilize the WeChat mini-games for RL research, in this article, we propose WemiEnv, a lightweight, easy-to-use and open-source platform for RL research towards WeChat mini-games. WemiEnv is built on the WeChat developer tools and allows RL agents to interact with the mini-games. WemiEnv also supports user-customized mini-games, requiring users to implement only a few interface functions within WemiEnv API. We also provide six popular mini-games: Space Fighter, Flip, 2048, Flappy Bird, Timberman, and Snake as ready-to-use tasks. Experiments were conducted with the OpenAI Spinning Up library for RL baselines on the provided tasks to test the usability of WemiEnv.