Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent
  Learning
Authors: name: Joseph Suárez, name: Phillip Isola, name: Kyoung Whan Choe, name: David Bloomin, name: Hao Xiang Li, name: Nikhil Pinnaparaju, name: Nishaanth Kanna, name: Daniel Scott, name: Ryan Sullivan, name: Rose S. Shuman, name: Lucas de Alcântara, name: Herbie Bradley, name: Louis Castricato, name: Kirsty You, name: Yuhao Jiang, name: Qimai Li, name: Jiaxin Chen, name: Xiaolong Zhu

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.