# Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks

arXiv: https://arxiv.org/abs/2001.12004 · HTML: https://mlsanity.com/paper/2001.12004/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning](https://mlsanity.com/paper/2311.03736/) (2023)
- [RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles](https://mlsanity.com/paper/2502.20065/) (2025)

## Abstract (arXiv)

Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targeted and demonstrated success on arcade, first person shooter (FPS), real-time strategy (RTS), and massive online battle arena (MOBA) games. Our work considers massively multiplayer online role-playing games (MMORPGs or MMOs), which capture several complexities of real-world learning that are not well modeled by any other game genre. We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO. We further demonstrate that standard policy gradient methods and simple baseline models can learn interesting emergent exploration and specialization behaviors in this setting.