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The Multi-Agent Reinforcement Learning in Malm"O (MARL"O) Competition

arXiv 1901.08129 ↗First posted 23 Jan 2019cs.AIcited by 14

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Abstract (arXiv)

Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types. The Multi-Agent Reinforcement Learning in Malm"O (MARL"O) competition is a new challenge that proposes research in this domain using multiple 3D games. The goal of this contest is to foster research in general agents that can learn across different games and opponent types, proposing a challenge as a milestone in the direction of Artificial General Intelligence.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2019.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 2; 2024: 2; 2025: 1.

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  1. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 469
  2. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  3. 2021The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priorscited by 40
  4. 2022MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learnedcited by 26
  5. 2021The MineRL BASALT Competition on Learning from Human Feedbackcited by 24
  6. 2024TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraftcited by 20
  7. 2024MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needscited by 16
  8. 2023Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competitioncited by 15
  9. 2025A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Gamescited by 9
  10. 2021Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Predictioncited by 8
  11. 2023BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Taskscited by 8
  12. 2022Retrospective on the 2021 BASALT Competition on Learning from Human Feedbackcited by 5
2 more
  1. 2021Towards robust and domain agnostic reinforcement learning competitionscited by 2
  2. 2022Graph Neural Network based Agent in Google Research Footballcited by 1