{"url":"/dataset/neorl2","name":"NeoRL-2","full_name":null,"description_markdown":"NeoRL-2 includes new task scenarios that better reflect real-world task properties and includes traditional control methods as the data-collecting method. In summary, our contributions are as follows:\r\n\r\n1. Tasks in NeoRL-2 cover a wider range of application domains, including robotics, aircraft, industrial pipelines, controllable nuclear fusion, healthcare, etc., encompassing key features such as delays, external factors, and safety constraints.\r\n\r\n2. The data-collecting method in NeoRL-2 better aligns with real-world scenarios, employing deterministic methods for sampling. In some specific tasks, classical feedback controllers, such as Proportional-Integral-Derivative (PID) controller, are introduced.\r\n\r\n3. We conducted experiments on these tasks using state-of-the-art (SOTA) offline RL algorithms and found that in most tasks, the trained policy of the current offline RL algorithms did not significantly outperform the behavior policy.\r\n\r\nBy extending near real-world tasks in NeoRL-2, we hope that the development and implementation of RL in real-world scenarios can take into account these challenges and tackle more realistic domains.","description_withheld":null,"homepage":"https://github.com/polixir/NeoRL2","introduced_date":"2024-06-07","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY 4.0","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"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NeoRL-2"],"data_loaders":[],"num_papers_in_archive":0,"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."}