In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL). Despite their success, the underlying games are usually implemented with their own preset environments and game mechanics, thus making it difficult for researchers to prototype different game environments. However, testing the RL agents against a variety of game environments is critical for recent effort to study generalization in RL and avoid the problem of overfitting that may otherwise occur. In this paper, we present Griddly as a new platform for Game AI research that provides a unique combination of highly configurable games, different observer types and an efficient C++ core engine. Additionally, we present a series of baseline experiments to study the effect of different observation configurations and generalization ability of RL agents.

翻译：近年来,游戏AI研究取得了巨大突破,特别是在加强学习(RL)方面。 尽管这些研究取得了成功,但基础游戏通常使用自己的预设环境和游戏机械进行,从而使研究人员难以对不同的游戏环境进行原型设计。然而,根据各种游戏环境测试RL代理剂对于最近努力研究RL的概括化和避免否则可能发生的超标问题至关重要。在本文中,我们将它作为一个新的游戏AI研究平台提出,它提供了高度可配置的游戏、不同观察类型和高效的C++核心引擎的独特组合。此外,我们提出一系列基线实验,以研究不同观测配置的影响和RL代理的概括化能力。