Griddly: A platform for AI research in games
  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.
