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.
@article{arxiv.2108.06148,
title = {Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments},
author = {Vasilii Davydov and Alexey Skrynnik and Konstantin Yakovlev and Aleksandr I. Panov},
journal= {arXiv preprint arXiv:2108.06148},
year = {2021}
}
Comments
This is a preprint of the paper accepted to RCAI 2021. It contains 11 pages and 5 figures