# POGEMA: Partially Observable Grid Environment for Multiple Agents

arXiv: https://arxiv.org/abs/2206.10944 · HTML: https://mlsanity.com/paper/2206.10944/

## 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 (most cited first, 11 of 11)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning](https://mlsanity.com/paper/2006.11751/) (2020)
- [MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence](https://mlsanity.com/paper/1712.00600/) (2017)
- [RLlib: Abstractions for Distributed Reinforcement Learning](https://mlsanity.com/paper/1712.09381/) (2017)
- [Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots](https://mlsanity.com/paper/1409.2399/) (2014)

## Built on by (most cited first, 6 of 6)

- [Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results](https://mlsanity.com/paper/2307.13453/) (2023)
- [Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding](https://mlsanity.com/paper/2308.05893/) (2023)
- [CAMAR: Continuous Actions Multi-Agent Routing](https://mlsanity.com/paper/2508.12845/) (2025)
- [Reinforcement Learning with Success Induced Task Prioritization](https://mlsanity.com/paper/2301.00691/) (2022)
- [Multi-Agent Path Finding via Offline RL and LLM Collaboration](https://mlsanity.com/paper/2509.22130/) (2025)
- [CoGrid & the Multi-User Gymnasium: A Framework for Multi-Agent Experimentation](https://mlsanity.com/paper/2604.15044/) (2026)

## 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.