{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-mmo-a-massively-multiagent-game","title":"Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents","arxiv_id":"1903.00784","date":"2019-03-02","proceeding":null,"authors":["Joseph Suarez","Yilun Du","Phillip Isola","Igor Mordatch"],"abstract":"The emergence of complex life on Earth is often attributed to the arms race\nthat ensued from a huge number of organisms all competing for finite resources.\nWe present an artificial intelligence research environment, inspired by the\nhuman game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games,\na.k.a. MMOs), that aims to simulate this setting in microcosm. As with MMORPGs\nand the real world alike, our environment is persistent and supports a large\nand variable number of agents. Our environment is well suited to the study of\nlarge-scale multiagent interaction: it requires that agents learn robust combat\nand navigation policies in the presence of large populations attempting to do\nthe same. Baseline experiments reveal that population size magnifies and\nincentivizes the development of skillful behaviors and results in agents that\noutcompete agents trained in smaller populations. We further show that the\npolicies of agents with unshared weights naturally diverge to fill different\nniches in order to avoid competition.","url_abs":"http://arxiv.org/abs/1903.00784v1","url_pdf":"http://arxiv.org/pdf/1903.00784v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-mmo-a-massively-multiagent-game","repo_url":"https://github.com/openai/neural-mmo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1903.00784","atlas_url":"https://app.syntology.ai/?focus=1903.00784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00784"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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