mlsanityData to 14 Oct 2026

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

arXiv 2311.03736 ↗First posted 7 Nov 2023cs.AI cs.LG cs.MAcited by 9

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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.

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

9 of 20 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020The NetHack Learning Environmentcited by 198
  3. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 99
  4. 2019Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agentscited by 44
  5. 2019TorchBeast: A PyTorch Platform for Distributed RLcited by 41
  6. 2021The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priorscited by 41
  7. 2020Griddly: A platform for AI research in gamescited by 31
  8. 2021The Neural MMO Platform for Massively Multiagent Researchcited by 17
  9. 2020Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networkscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 2.

9 papers.

  1. 2024POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfindingcited by 21
  2. 2024The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Designcited by 11
  3. 2024Autoverse: An Evolvable Game Language for Learning Robust Embodied Agentscited by 3
  4. 2024Syllabus: Portable Curricula for Reinforcement Learning Agentscited by 2
  5. 2026Benchmarking Open-Ended Multi-Agent Coordination in Language Agentscited by 2
  6. 2025Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Reviewcited by 1
  7. 2025The Emergence of Complex Behavior in Large-Scale Ecological Environmentscited by 1
  8. 2024Massively Multiagent Minigames for Training Generalist Agentscited by 0
  9. 2026Feedback Attribution and Representation Geometry: Metrics for Comparing Individual and Shared Rewards in MARLcited by 0