# Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments

arXiv: https://arxiv.org/abs/2108.06148 · HTML: https://mlsanity.com/paper/2108.06148/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018)
- [Online and Offline Reinforcement Learning by Planning with a Learned Model](https://mlsanity.com/paper/2104.06294/) (2021)
- [Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots](https://mlsanity.com/paper/1409.2399/) (2014)
- [Prioritized Multi-agent Path Finding for Differential Drive Robots](https://mlsanity.com/paper/1911.10578/) (2019)
- [Delta Schema Network in Model-based Reinforcement Learning](https://mlsanity.com/paper/2006.09950/) (2020)

## Built on by (most cited first, 1 of 1)

- [Efficient Policy Space Response Oracles](https://mlsanity.com/paper/2202.00633/) (2022)

## Abstract (arXiv)

In this paper, we consider the problem of multi-agent navigation in partially observable grid environments. This problem is challenging for centralized planning approaches as they, typically, rely on the full knowledge of the environment. We suggest utilizing the reinforcement learning approach when the agents, first, learn the policies that map observations to actions and then follow these policies to reach their goals. To tackle the challenge associated with learning cooperative behavior, i.e. in many cases agents need to yield to each other to accomplish a mission, we use a mixing Q-network that complements learning individual policies. In the experimental evaluation, we show that such approach leads to plausible results and scales well to large number of agents.