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DeepMind recently open-sourced a software system called Lab2D. Lab2D is designed to support the creation of 2D environments for artificial intelligence and machine learning research.
The DeepMind team states that 2D environments are naturally more straightforward to understand than 3D environments at a little loss of expressiveness. The researchers say that even a simple game Pong, which consists of three moving rectangles on a black background, can capture something primary about table tennis’s real game. This abstraction superficially makes it simpler to grasp the nature of problems and concepts in artificial intelligence.
The researchers assert that deep complexity accompanying numerous dimensions can be studied easily in 2D as in 3D. Besides, 2D environments are significantly less resource-intensive to operate and typically do not need specialized hardware, such as GPUs, to attain acceptable performance. 2D environments have been successfully used to study various diverse problems such as social complexity, navigation, imperfect information, abstract reasoning, exploration, etc.
Lab2D facilitates the creation of 2D, layered, discrete “grid-world” environments in which each piece (similar to chess pieces) moves around. This system is specially tailored for multi-agent reinforcement learning. It supports multiple players interacting …
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