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.

DeepMind this week open-sourced Lab2D, a software system designed to support the creation of 2D environments for AI and machine learning research. The Alphabet subsidiary says that Lab2D was built with the needs of deep reinforcement learning researchers in mind, but that it can be useful beyond that particular subfield of machine learning. The DeepMind team behind Lab2D makes the case that 2D environments are inherently easier to understand than 3D ones at little loss of expressiveness. Even a game as simple as Pong, which essentially consists of three moving rectangles on a black background, can capture something fundamental about the real game of table tennis, the researchers assert. This abstraction ostensibly makes it easier to capture the essence of problems and concepts in AI. "Rich complexity along numerous dimensions can be studied in 2D just as readily as in 3D, if not more so … In addition, 2D worlds are significantly less resource-intensive to run, and typically do not require any specialized hardware (like GPUs) to attain reasonable performance," the researchers continued in their paper describing Lab2D. "2D worlds have been successfully used to study problems as diverse as social complexity, navigation, imperfect information, abstract reasoning, exploration, and many more."