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MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

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arxiv 2202.10583 v1 pith:5GPCFGR3 submitted 2022-02-17 cs.LG cs.AI

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

classification cs.LG cs.AI
keywords trackdiamondminerlparticipantscompetitionincreasedmethodspromote
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enforce the use of general techniques, the use of sample-efficient methods, and the reproducibility of the results. While beneficial for the research community, these restrictions come at a cost -- increased difficulty. If the barrier for entry is too high, many potential participants are demoralized. With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers. With this track and more extensive tutorials and support, we saw an increased number of submissions. The participants of this easier track were able to obtain a diamond, and the participants of the harder track progressed the generalizable solutions in the same task.

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