mlsanityData to 7 Oct 2026

The Neural MMO Platform for Massively Multiagent Research

arXiv 2110.07594 ↗First posted 14 Oct 2021cs.LG cs.AI cs.MAcited by 17

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Abstract (arXiv)

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first to combine them all. We present Neural MMO as free and open source software with active support, ongoing development, documentation, and additional training, logging, and visualization tools to help users adapt to this new setting. Initial baselines on the platform demonstrate that agents trained in large populations explore more and learn a progression of skills. We raise other more difficult problems such as many-team cooperation as open research questions which Neural MMO is well-suited to answer. Finally, we discuss current limitations of the platform, potential mitigations, and plans for continued development.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

20 of 33 references.

References · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  3. 2016OpenAI Gymcited by 1,684
  4. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  5. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  6. 2018DeepMind Control Suitecited by 935
  7. 2019Solving Rubik's Cube with a Robot Handcited by 699
  8. 2019The StarCraft Multi-Agent Challengecited by 589
  9. 2019Leveraging Procedural Generation to Benchmark Reinforcement Learningcited by 478
  10. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 308
  11. 2018Human-level performance in first-person multiplayer games with population-based deep reinforcement learningcited by 260
  12. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 257
8 more
  1. 2018Unity: A General Platform for Intelligent Agentscited by 232
  2. 2017MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligencecited by 69
  3. 2018Gotta Learn Fast: A New Benchmark for Generalization in RLcited by 59
  4. 2017RLlib: Abstractions for Distributed Reinforcement Learningcited by 53
  5. 2019Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agentscited by 42
  6. 2021The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priorscited by 40
  7. 2020Griddly: A platform for AI research in gamescited by 31
  8. 2018Pommerman: A Multi-Agent Playgroundcited by 30

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 7; 2024: 4; 2025: 3; 2026: 1.

17 papers.

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  1. 2024PufferLib: Making Reinforcement Learning Libraries and Environments Play Nicecited by 22
  2. 2022MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Librarycited by 21
  3. 2024TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraftcited by 20
  4. 2023Games for Artificial Intelligence Research: A Review and Perspectivescited by 14
  5. 2024FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learningcited by 13
  6. 2022Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RLcited by 11
  7. 2024The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Designcited by 11
  8. 2023Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learningcited by 9
  9. 2023Emergent collective intelligence from massive-agent cooperation and competitioncited by 3
  10. 2023Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rulescited by 2
  11. 2023Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Frameworkcited by 2
  12. 2023Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Gamescited by 1
5 more
  1. 2025Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantagecited by 1
  2. 2023Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMOcited by 0
  3. 2025Inclusive Fitness as a Key Step Towards More Advanced Social Behaviors in Multi-Agent Reinforcement Learning Settingscited by 0
  4. 2025StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigmcited by 0
  5. 2026Reinforcement Learning: From Algorithms To Foundation Modelscited by 0