DeepMind open-sources Lab2D, a grid-based environment for reinforcement learning research

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."