{"url":"/dataset/momaland","name":"MOMAland","full_name":null,"description_markdown":"MOMAland is an open source Python library for developing and comparing multi-objective multi-agent reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API.","description_withheld":null,"homepage":"https://momaland.farama.org/","introduced_date":"2024-07-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/momaland-a-set-of-benchmarks-for-multi","title":"MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning","first_author":"Florian Felten","url":null},"license":{"name":"GPL 3.0","url":null},"modalities":[],"tasks":[{"name":"Multi-Objective Multi-Agent Reinforcement Learning","url":"/task/multi-objective-multi-agent-reinforcement","datasets_with_task":"/datasets/task/multi-objective-multi-agent-reinforcement"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MOMAland"],"data_loaders":[],"num_papers_in_archive":2,"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."}