{"url":"/dataset/nocturne","name":"Nocturne","full_name":null,"description_markdown":"Nocturne is  a 2D, partially observed, driving simulator, built in C++ for speed and exported as a Python library.\r\n\r\nIt is currently designed to handle traffic scenarios from the Waymo Open Dataset, and with some work could be extended to support different driving datasets. Using the Python library nocturne, one is able to train controllers for AVs to solve various tasks from the Waymo dataset, which we provide as a benchmark, then use the tools we offer to evaluate the designed controllers.\r\n\r\nUsing this rich data source, Nocturne contains a wide range of scenarios whose solution requires the formation of complex coordination, theory of mind, and handling of partial observability. Below we show replays of the expert data, centered on the light blue agent, with the corresponding view of the agent on the right.\r\n\r\nDescription from: [Nocturne](https://github.com/facebookresearch/nocturne)","description_withheld":null,"homepage":"https://github.com/facebookresearch/nocturne","introduced_date":"2022-06-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/nocturne-a-scalable-driving-benchmark-for","title":"Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world","first_author":"Eugene Vinitsky","url":null},"license":{"name":"MIT License","url":"https://github.com/facebookresearch/nocturne/blob/main/LICENSE"},"modalities":[],"tasks":[],"languages":[],"variants":["Nocturne"],"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."}