OpenAI spent $160,000 on Upwork for Minecraft gamers to train a neural net

From the video of VPT pursuing the making of a diamong pickaxe in Minecraft. The computer program achieved the feat in ten minutes, half the time it would take a proficient human player to do it.

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In a paper unveiled this week, "Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos," OpenAI researchers Bowen Baker and team break ground in the use of large datasets to train a neural network to mimic human keystrokes to solve different tasks in the video game. (A blog post has also been posted by OpenAI.) 

A plethora of neural networks have conquered various types of games via what's called reinforcement learning in recent years, including DeepMind DeepMind's AlphaZero, which took on chess, Go, and Shogi, and the subsequent MuZero program, which added the ability to handle Atari games. 

Baker and team wanted to develop a neural network for the more complex "open world" game environment of Minecraft, where an array of keystrokes allow players far greater degrees of freedom than in chess or Atari games. 

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