mlsanityData to 14 Oct 2026

POGEMA: Partially Observable Grid Environment for Multiple Agents

arXiv 2206.10944 ↗First posted 22 Jun 2022cs.LG cs.AI cs.MAcited by 6

Not on the map. Why?

Abstract (arXiv)

We introduce POGEMA (https://github.com/AIRI-Institute/pogema) a sandbox for challenging partially observable multi-agent pathfinding (PO-MAPF) problems . This is a grid-based environment that was specifically designed to be a flexible, tunable and scalable benchmark. It can be tailored to a variety of PO-MAPF, which can serve as an excellent testing ground for planning and learning methods, and their combination, which will allow us to move towards filling the gap between AI planning and learning.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 22 Jun 2022.

Builds on

11 of 17 references.

References · 11

  1. 2016OpenAI Gymcited by 1,736
  2. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  3. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  4. 2019The StarCraft Multi-Agent Challengecited by 597
  5. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  6. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
  7. 2019Multi-Agent Pathfinding: Definitions, Variants, and Benchmarkscited by 199
  8. 2020Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learningcited by 93
  9. 2017MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligencecited by 71
  10. 2017RLlib: Abstractions for Distributed Reinforcement Learningcited by 54
  11. 2014Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robotscited by 19

Built on by

Cited by year (2026 to 14 Oct)

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

6 papers.

  1. 2023Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Resultscited by 9
  2. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 6
  3. 2025CAMAR: Continuous Actions Multi-Agent Routingcited by 2
  4. 2022Reinforcement Learning with Success Induced Task Prioritizationcited by 1
  5. 2025Multi-Agent Path Finding via Offline RL and LLM Collaborationcited by 0
  6. 2026CoGrid & the Multi-User Gymnasium: A Framework for Multi-Agent Experimentationcited by 0