{"url":"/dataset/smacv2","name":"SMACv2","full_name":null,"description_markdown":"**SMACv2** (StarCraft Multi-Agent Challenge v2) is a new version of the benchmark where scenarios are procedurally generated and require agents to generalise to previously unseen settings (from the same distribution) during evaluation.\r\n\r\nSource: [SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning](https://arxiv.org/pdf/2212.07489v1.pdf)\r\n\r\nImage Source: [https://github.com/oxwhirl/smacv2](https://github.com/oxwhirl/smacv2)","description_withheld":null,"homepage":"https://sites.google.com/view/smacv2","introduced_date":"2022-12-14","introduced_date_note":null,"introduced_by":null,"license":{"name":"MIT License","url":"https://github.com/oxwhirl/smacv2/blob/main/LICENSE"},"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[{"name":"Reinforcement Learning (RL)","url":"/task/reinforcement-learning-1","datasets_with_task":"/datasets/task/reinforcement-learning-1"}],"languages":[],"variants":["SMACv2"],"data_loaders":[],"num_papers_in_archive":30,"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."}