Toy Block Manufacturing Process Optimization AI Competition

Abstract

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. Many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods.

This competition is designed to foster the development of algorithms which can drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments using human demonstrations. Participants compete to develop systems which solve a hard task in Minecraft, obtaining a diamond, with a limited number of samples.

Task

Some of the stages of obtaining a diamond: obtaining wood, a stone pickaxe, iron, and diamond.

This competition uses a set of Gym environments based on 

Malmo

. The environment and dataset loader is available through a pip package. See 

here

 for documentation of the environment and accessing the data.

The task of the competition is solving the MineRLObtainDiamond environment. In this environment, the agent begins in a random starting location without any items, and is tasked with obtaining a diamond. This task can only be accomplished by navigating the complex item hierarchy of Minecraft.

https://www.aicrowd.com/challenges/neurips-2019-minerl-competition

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