Notes

Minecraft Diamond Challenge Leaves AI Creators Stumped

Minecraft diamond challenge leaves AI creators confused By Sam Shead Technology reporter



12 December 2019



Most Minecraft players can find the diamonds that are the most important in just a few minutes. However, it is much more difficult to train artificial intelligence to do this task.



In the summer, Minecraft publisher Microsoft and other companies challenged coders to develop AI agents that could find the sought-after gems.



Most people can master it in their first session.



However, none of the more than 660 entries submitted was up to the requirements.



The official announcement of the results of the MineRL competition which is the word that means "mineral", will be made at the NeurIPSAI conference in Vancouver, Canada on Saturday.



The objective was to see whether the problem could be solved without requiring an enormous amount of computing power.



Despite the absence of a winner One of the organisers said she was still "hugely impressed" by a few participants.
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Katja Hofmann, the principal researcher at Microsoft Research, stated that "the task we set was very challenging." "Finding diamonds in Minecraft involves a number of steps - from cutting trees, to making tools to exploring caves and actually finding diamonds.



"While no submitted agent has fully solved the task but they have made a lot of progress and learned to make some of the tools required to accomplish the task."



Mining diamonds



Minecraft has become wildly popular since it was released in 2011.



More than 180 million copies of the game that is open-world have been sold, and the game has more than 112 million active monthly players.



Diamond is a very important resource in Minecraft. It can be used to create powerful weapons and armor.



However, in order to get the precious stone, the player must first complete a couple of other steps.



Jules Portelly, a Minecraft player, said it shouldn't take more than 20 minutes to earn your first diamonds if you're comfortable with the game.



The entry requirements were that the applicants could only utilize one graphics processing unit (GPU), and they must complete four days of training. AI systems usually require months or even years of gaming time to master games like StarCraft II.



A relatively small Minecraft dataset, with 60 million frames of human player data, was provided to players to help them train their systems.



"At the start of every episode they spawned in a procedurally-generated Minecraft world," explained Dr Hofmann.



"So they had to understand the idea of finding resources, making tools and finding a treasure."



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The developers were asked to create programs that could be learned by imitation, a technique called "imitation learning".



This is where you try to get AI agents to take the best approach to solve a problem by making them imitate humans or other software.



This contrasts with "reinforcement learning" where agents are trained to find the most effective solution by trial and error.



AI for all



Researchers have discovered that using reinforcement learning by itself can produce superior results.



For instance DeepMind's AlphaGo Zero program trumped one of the research hub's previous efforts, which used both reinforcement learning and the study of data labelled from human play to learn the game of Go.



But this "pure" method usually requires a lot more computing power, which makes it too expensive for researchers who are not large organizations or government agencies.



William Guss, a Carnegie Mellon University PhD student, was the main organizer of the competition. He said that the purpose of the competition was to prove that "throwing huge computations at problems isn't necessarily the right way to push the boundaries of technology as a field."



He added, "It works against democratising access to these systems of reinforcement learning and leaves the capability for agents to learn in complex environments to large corporations with vast computing resources."



However, the outcome could be a way to highlight the advantages these well-funded organizations have.



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