MineRL
Introduced by Guss et al. in The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
MineRLis an imitation learning dataset with over 60 million frames of recorded human player data. The dataset includes a set of tasks which highlights many of the hardest problems in modern-day Reinforcement Learning: sparse rewards and hierarchical policies.
Source: MineRLPapers
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