{"url":"/dataset/minerl","name":"MineRL","full_name":"MineRL","description_markdown":"**MineRL**is 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.\r\n\r\nSource: [MineRL](https://minerl.io/)","description_withheld":null,"homepage":"https://minerl.io/","introduced_date":"2019-04-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-minerl-competition-on-sample-efficient","title":"The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors","first_author":"William H. Guss","url":null},"license":null,"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[{"name":"Visual Navigation","url":"/task/visual-navigation","datasets_with_task":"/datasets/task/visual-navigation"},{"name":"Imitation Learning","url":"/task/imitation-learning","datasets_with_task":"/datasets/task/imitation-learning"}],"languages":[],"variants":["MineRL"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}