title: Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent
    Learning
summary: Neural MMO 2.0 is a massively multi-agent environment for reinforcement
  learning research. The key feature of this new version is a flexible task
  system that allows users to define a broad range of objectives and reward
  signals. We challenge researchers to train agents capable of generalizing to
  tasks, maps, and opponents never seen during training. Neural MMO features
  procedurally generated maps with 128 agents in the standard setting and support
  for up to. Version 2.0 is a complete rewrite of its predecessor with three-fold
  improved performance and compatibility with CleanRL. We release the platform as
  free and open-source software with comprehensive documentation available at
  neuralmmo.github.io and an active community Discord. To spark initial research
  on this new platform, we are concurrently running a competition at NeurIPS
  2023.
paper_authors._id: 654b095efa25dfb780b783bb
paper_authors.name: Joseph Suárez
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-11-09T15:59:02.834Z
paper_authors._id: 654cffdf5d43cf730dfa9e3c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/K860p3u1oU44kl5hCIBlO.jpeg
paper_authors.fullname: Joseph Suarez
paper_authors.type: user
paper_authors.user: jsuarez5341
paper_authors._id: 654b095efa25dfb780b783bc
paper_authors.name: Phillip Isola
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-11-08T04:24:24.661Z
paper_authors._id: 623cb1f3ca87989bbf797eee
paper_authors.avatarUrl: /avatars/13fbd5b123b8676561751f820317600c.svg
paper_authors.fullname: Phillip Isola
paper_authors.type: user
paper_authors.user: phillipi
paper_authors._id: 654b095efa25dfb780b783bd
paper_authors.name: Kyoung Whan Choe
paper_authors._id: 654b095efa25dfb780b783be
paper_authors.name: David Bloomin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-08T08:54:22.420Z
paper_authors._id: 636b0258c95145940b019fe2
paper_authors.avatarUrl: /avatars/c6b363ef3228ed29e4a2efe73285c758.svg
paper_authors.fullname: David Bloomin
paper_authors.type: user
paper_authors.user: daveey
paper_authors._id: 654b095efa25dfb780b783bf
paper_authors.name: Hao Xiang Li
paper_authors._id: 654b095efa25dfb780b783c0
paper_authors.name: Nikhil Pinnaparaju
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-11-08T04:13:29.794Z
paper_authors._id: 60e9a9b60f5f3b25fc66a05d
paper_authors.avatarUrl: /avatars/1da08c44426927add9a5fda831f22de1.svg
paper_authors.fullname: Nikhil Pinnaparaju
paper_authors.type: user
paper_authors.user: nikhilpinnaparaju
paper_authors._id: 654b095efa25dfb780b783c1
paper_authors.name: Nishaanth Kanna
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-08T08:54:45.216Z
paper_authors._id: 62ba79276501b0ff15bb8d4f
paper_authors.avatarUrl: /avatars/861b9324df89a0a5e467f50a595da30b.svg
paper_authors.fullname: Nishaanth Kanna
paper_authors.type: user
paper_authors.user: nishaanthkanna
paper_authors._id: 654b095efa25dfb780b783c2
paper_authors.name: Daniel Scott
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-11-08T17:06:07.257Z
paper_authors._id: 6383f50a5c83390fc7567cfa
paper_authors.avatarUrl: /avatars/cfc5c3e4dc8c5e4284080208a2217b2a.svg
paper_authors.fullname: Daniel Scott
paper_authors.type: user
paper_authors.user: dscott
paper_authors._id: 654b095efa25dfb780b783c3
paper_authors.name: Ryan Sullivan
paper_authors._id: 654b095efa25dfb780b783c4
paper_authors.name: Rose S. Shuman
paper_authors._id: 654b095efa25dfb780b783c5
paper_authors.name: Lucas de Alcântara
paper_authors._id: 654b095efa25dfb780b783c6
paper_authors.name: Herbie Bradley
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-08T08:59:45.745Z
paper_authors._id: 62f79f8147d782a6e28440f9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f79f8147d782a6e28440f9/L-27MtgYpD1XQOBnx1gzL.jpeg
paper_authors.fullname: Herbie Bradley
paper_authors.type: user
paper_authors.user: HyperionHF
paper_authors._id: 654b095efa25dfb780b783c7
paper_authors.name: Louis Castricato
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-08T08:59:37.905Z
paper_authors._id: 60d35a0307da9c17c7270909
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1665155571839-60d35a0307da9c17c7270909.jpeg
paper_authors.fullname: Louis Castricato
paper_authors.type: user
paper_authors.user: LouisCastricato
paper_authors._id: 654b095efa25dfb780b783c8
paper_authors.name: Kirsty You
paper_authors._id: 654b095efa25dfb780b783c9
paper_authors.name: Yuhao Jiang
paper_authors._id: 654b095efa25dfb780b783ca
paper_authors.name: Qimai Li
paper_authors._id: 654b095efa25dfb780b783cb
paper_authors.name: Jiaxin Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-01-24T07:57:50.822Z
paper_authors._id: 65b0c2e69d4b4d79309d661e
paper_authors.avatarUrl: /avatars/7675a23b65bedebe963822e3444195cc.svg
paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: jiaxin0606
paper_authors._id: 654b095efa25dfb780b783cc
paper_authors.name: Xiaolong Zhu
paper_title: Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent
    Learning
paper_summary: Neural MMO 2.0 is a massively multi-agent environment for reinforcement
  learning research. The key feature of this new version is a flexible task
  system that allows users to define a broad range of objectives and reward
  signals. We challenge researchers to train agents capable of generalizing to
  tasks, maps, and opponents never seen during training. Neural MMO features
  procedurally generated maps with 128 agents in the standard setting and support
  for up to. Version 2.0 is a complete rewrite of its predecessor with three-fold
  improved performance and compatibility with CleanRL. We release the platform as
  free and open-source software with comprehensive documentation available at
  neuralmmo.github.io and an active community Discord. To spark initial research
  on this new platform, we are concurrently running a competition at NeurIPS
  2023.
paper_ai_summary: A flexible reinforcement learning environment with procedurally generated maps supports generalization to new tasks, maps, and opponents, featuring improved performance and compatibility with CleanRL.
paper_ai_keywords: neural MMO reinforcement learning multi-agent environment task system reward signals procedural generation generalization CleanRL