mlsanityData to 7 Oct 2026

DeepMind Lab2D

arXiv 2011.07027 ↗First posted 13 Nov 2020cs.AIcited by 12

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Abstract (arXiv)

We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind Lab2D was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, but it may also be useful beyond that particular subfield.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 2; 2023: 0; 2024: 2; 2025: 1; 2026: 1.

12 papers.

  1. 2021Collaborating with Humans without Human Datacited by 139
  2. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  3. 2021Quantifying the effects of environment and population diversity in multi-agent reinforcement learningcited by 21
  4. 2024FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learningcited by 13
  5. 2022Warmth and competence in human-agent cooperationcited by 11
  6. 2025SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 7
  7. 2021A multi-agent reinforcement learning model of reputation and cooperation in human groupscited by 6
  8. 2021Agent-Centric Representations for Multi-Agent Reinforcement Learningcited by 6
  9. 2022Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibriacited by 6
  10. 2021Statistical discrimination in learning agentscited by 4
  11. 2024AMaze: An intuitive benchmark generator for fast prototyping of generalizable agentscited by 1
  12. 2026Reinforcement Learning: From Algorithms To Foundation Modelscited by 0