# Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents

arXiv: https://arxiv.org/abs/1903.00784 · HTML: https://mlsanity.com/paper/1903.00784/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 30 of 44)

- [Building Cooperative Embodied Agents Modularly with Large Language Models](https://mlsanity.com/paper/2307.02485/) (2023)
- [Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots](https://mlsanity.com/paper/2310.13724/) (2023)
- [SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.07489/) (2022)
- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [COMBO: Compositional World Models for Embodied Multi-Agent Cooperation](https://mlsanity.com/paper/2404.10775/) (2024)
- [CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents](https://mlsanity.com/paper/2401.10568/) (2024)
- [Automating the Search for Artificial Life with Foundation Models](https://mlsanity.com/paper/2412.17799/) (2024)
- [The Neural MMO Platform for Massively Multiagent Research](https://mlsanity.com/paper/2110.07594/) (2021)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge](https://mlsanity.com/paper/2411.01796/) (2024)
- [Virtual Community: An Open World for Humans, Robots, and Society](https://mlsanity.com/paper/2508.14893/) (2025)
- [Biomaker CA: a Biome Maker project using Cellular Automata](https://mlsanity.com/paper/2307.09320/) (2023)
- [Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning](https://mlsanity.com/paper/2311.03736/) (2023)
- [RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games](https://mlsanity.com/paper/2307.01676/) (2023)
- [Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2108.12988/) (2021)
- [Powderworld: A Platform for Understanding Generalization via Rich Task Distributions](https://mlsanity.com/paper/2211.13051/) (2022)
- [JaxLife: An Open-Ended Agentic Simulator](https://mlsanity.com/paper/2409.00853/) (2024)
- [OPEn: An Open-ended Physics Environment for Learning Without a Task](https://mlsanity.com/paper/2110.06912/) (2021)
- [Balancing of competitive two-player Game Levels with Reinforcement Learning](https://mlsanity.com/paper/2306.04429/) (2023)
- [Crafting Desirable Climate Trajectories with RL Explored Socio-Environmental Simulations](https://mlsanity.com/paper/2410.07287/) (2024)
- [Ella: Embodied Social Agents with Lifelong Memory](https://mlsanity.com/paper/2506.24019/) (2025)
- [Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments](https://mlsanity.com/paper/2102.12307/) (2021)
- [Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity](https://mlsanity.com/paper/2107.05786/) (2021)
- [An Efficient Open World Environment for Multi-Agent Social Learning](https://mlsanity.com/paper/2508.15679/) (2025)
- [Towards a Better Understanding of Learning with Multiagent Teams](https://mlsanity.com/paper/2306.16205/) (2023)
- [Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework](https://mlsanity.com/paper/2310.09952/) (2023)
- [Syllabus: Portable Curricula for Reinforcement Learning Agents](https://mlsanity.com/paper/2411.11318/) (2024)
- [Multi-Agent Craftax: Benchmarking Open-Ended Multi-Agent Reinforcement Learning at the Hyperscale](https://mlsanity.com/paper/2511.04904/) (2025)
- [Benchmarking Open-Ended Multi-Agent Coordination in Language Agents](https://mlsanity.com/paper/2606.08340/) (2026)
- [Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams](https://mlsanity.com/paper/2304.07337/) (2023)

## Abstract (arXiv)

The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial intelligence research environment, inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs), that aims to simulate this setting in microcosm. As with MMORPGs and the real world alike, our environment is persistent and supports a large and variable number of agents. Our environment is well suited to the study of large-scale multiagent interaction: it requires that agents learn robust combat and navigation policies in the presence of large populations attempting to do the same. Baseline experiments reveal that population size magnifies and incentivizes the development of skillful behaviors and results in agents that outcompete agents trained in smaller populations. We further show that the policies of agents with unshared weights naturally diverge to fill different niches in order to avoid competition.