# Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning

arXiv: https://arxiv.org/abs/2311.03736 · HTML: https://mlsanity.com/paper/2311.03736/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The NetHack Learning Environment](https://mlsanity.com/paper/2006.13760/) (2020)
- [Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot](https://mlsanity.com/paper/2107.06857/) (2021)
- [Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents](https://mlsanity.com/paper/1903.00784/) (2019)
- [TorchBeast: A PyTorch Platform for Distributed RL](https://mlsanity.com/paper/1910.03552/) (2019)
- [The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/2101.11071/) (2021)
- [Griddly: A platform for AI research in games](https://mlsanity.com/paper/2011.06363/) (2020)
- [The Neural MMO Platform for Massively Multiagent Research](https://mlsanity.com/paper/2110.07594/) (2021)
- [Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks](https://mlsanity.com/paper/2001.12004/) (2020)

## Built on by (most cited first, 9 of 9)

- [POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding](https://mlsanity.com/paper/2407.14931/) (2024)
- [The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design](https://mlsanity.com/paper/2406.17949/) (2024)
- [Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents](https://mlsanity.com/paper/2407.04221/) (2024)
- [Syllabus: Portable Curricula for Reinforcement Learning Agents](https://mlsanity.com/paper/2411.11318/) (2024)
- [Benchmarking Open-Ended Multi-Agent Coordination in Language Agents](https://mlsanity.com/paper/2606.08340/) (2026)
- [Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review](https://mlsanity.com/paper/2507.10142/) (2025)
- [The Emergence of Complex Behavior in Large-Scale Ecological Environments](https://mlsanity.com/paper/2510.18221/) (2025)
- [Massively Multiagent Minigames for Training Generalist Agents](https://mlsanity.com/paper/2406.05071/) (2024)
- [Feedback Attribution and Representation Geometry: Metrics for Comparing Individual and Shared Rewards in MARL](https://mlsanity.com/paper/2607.16524/) (2026)

## Abstract (arXiv)

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.