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Playing Minecraft with Behavioural Cloning

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arxiv 2005.03374 v1 pith:7I5LTK5T submitted 2020-05-07 cs.AI cs.LG

Anssi Kanervisto , Janne Karttunen , Ville Hautamäki This is my paper · ORCID
classification cs.AIcs.LG
keywords behaviouralcloningcompetitionhumanminecraftperformancetrainingactions
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MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment. We approached this task with behavioural cloning by predicting what actions human players would take, and reached fifth place in the final ranking. Despite being a simple algorithm, we observed the performance of such an approach can vary significantly, based on when the training is stopped. In this paper, we detail our submission to the competition, run further experiments to study how performance varied over training and study how different engineering decisions affected these results.

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