mlsanityData to 7 Oct 2026

Pommerman: A Multi-Agent Playground

arXiv 1809.07124 ↗First posted 19 Sep 2018cs.MAcited by 30

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Abstract (arXiv)

We present Pommerman, a multi-agent environment based on the classic console game Bomberman. Pommerman consists of a set of scenarios, each having at least four players and containing both cooperative and competitive aspects. We believe that success in Pommerman will require a diverse set of tools and methods, including planning, opponent/teammate modeling, game theory, and communication, and consequently can serve well as a multi-agent benchmark. To date, we have already hosted one competition, and our next one will be featured in the NIPS 2018 competition track.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2018.

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Citing papers per year: 2020: 1; 2021: 4; 2022: 10; 2023: 6; 2024: 4; 2025: 3; 2026: 2.

30 papers.

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  1. 2023Building Cooperative Embodied Agents Modularly with Large Language Modelscited by 231
  2. 2023Habitat 3.0: A Co-Habitat for Humans, Avatars and Robotscited by 220
  3. 2022SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learningcited by 118
  4. 2024Embodied LLM Agents Learn to Cooperate in Organized Teamscited by 81
  5. 2022VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learningcited by 63
  6. 2026A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agentscited by 47
  7. 2020Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimizationcited by 22
  8. 2022Any-Play: An Intrinsic Augmentation for Zero-Shot Coordinationcited by 22
  9. 2022MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Librarycited by 21
  10. 2021The Neural MMO Platform for Massively Multiagent Researchcited by 17
  11. 2023Games for Artificial Intelligence Research: A Review and Perspectivescited by 14
  12. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 11
18 more
  1. 2022Efficient Exploration in Resource-Restricted Reinforcement Learningcited by 10
  2. 2025MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learningcited by 8
  3. 2021Multi-Agent Advisor Q-Learningcited by 4
  4. 2022Self-Imitation Learning from Demonstrationscited by 4
  5. 2024Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chesscited by 4
  6. 2024Multi-Agent Training for Pommerman: Curriculum Learning and Population-based Self-Play Approachcited by 3
  7. 2022Decision-making with Speculative Opponent Modelscited by 2
  8. 2021Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemiccited by 1
  9. 2022Developing a Successful Bomberman Agentcited by 1
  10. 2022Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competitioncited by 1
  11. 2021A Fast Evolutionary adaptation for MCTS in Pommermancited by 0
  12. 2023Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learningcited by 0
  13. 2023Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommermancited by 0
  14. 2023The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Tradecited by 0
  15. 2024Effective Reward Specification in Deep Reinforcement Learningcited by 0
  16. 2025Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Reviewcited by 0
  17. 2025StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigmcited by 0
  18. 2026Reinforcement Learning: From Algorithms To Foundation Modelscited by 0