{"url":"/dataset/neorl","name":"NeoRL","full_name":null,"description_markdown":"- **NeoRL** is a collection of environments and datasets for offline reinforcement learning with a special focus on real-world applications. The design follows real-world properties like the conservative of behavior policies, limited amounts of data, high-dimensional state and action spaces, and the highly stochastic nature of the environments.\r\n- The datasets include robotics, industrial control, finance trading and city management tasks with real-world properties, containing three-level sizes of dataset, three-level quality of data to mimic the dataset we will meet in offline RL scenarios. \r\n- Users can use the dataset to evaluate offline RL algorithms with near real-world application nature.","description_withheld":null,"homepage":"http://polixir.ai/research/neorl","introduced_date":"2021-02-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/near-real-world-benchmarks-for-offline","title":"NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning","first_author":"Rongjun Qin","url":null},"license":{"name":"CC BY","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[{"name":"Offline RL","url":"/task/offline-rl","datasets_with_task":"/datasets/task/offline-rl"},{"name":"MuJoCo","url":"/task/mujoco","datasets_with_task":"/datasets/task/mujoco"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["NeoRL"],"data_loaders":[],"num_papers_in_archive":10,"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."}