Last week, Alphabet subsidiary, DeepMind open-sourced Lab2D, which the researchers explained to be a scalable environment simulator for artificial intelligence research, helping them to create 2D environments for AI and ML research. Researchers claim that it facilitates researcher-led experimentation with environment design while also helping them understand the influence of environments in multi-agent reinforcement learning. While it was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, it can be used beyond that particular subfield. In this article, we take a deeper look into what DeepMind Lab2D is all about and how it can help AI researchers. As researchers explain in the paper, DeepMind Lab2D (or "DMLab2D" for short) is a platform for the creation of two-dimensional, layered, discrete "grid-world" environments, in which the pieces -- which can be compared to chess pieces on a chessboard -- move around. This system is particularly tailored for multi-agent reinforcement learning.

We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind Lab2D was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, but it may also be useful beyond that particular subfield.