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

Davydov V., Skrynnik A., Yakovlev K., Panov A.
Тип документа: Book Chapter
Дата публикации: 2021-01-01
Название журнала: Lecture Notes in Computer Science
Издатель: Springer Nature
Квартиль: Q3
ISSN: 03029743, 16113349
DOI:  10.1007/978-3-030-86855-0_12
Краткое описание
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.
Пристатейные ссылки: 19
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1. Davydov V. и др. Q-Mixing Network for Multi-agent Pathfinding in Partially Observable Grid Environments // Lecture Notes in Computer Science. 2021. С. 169–179.
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TY - GENERIC

DO - 10.1007/978-3-030-86855-0_12

UR - http://dx.doi.org/10.1007/978-3-030-86855-0_12

TI - Q-Mixing Network for Multi-agent Pathfinding in Partially Observable Grid Environments

T2 - Artificial Intelligence

AU - Davydov, Vasilii

AU - Skrynnik, Alexey

AU - Yakovlev, Konstantin

AU - Panov, Aleksandr

PY - 2021

PB - Springer International Publishing

SP - 169-179

SN - 0302-9743

SN - 1611-3349

ER -

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@incollection{Davydov_2021,

doi = {10.1007/978-3-030-86855-0_12},

url = {https://doi.org/10.1007%2F978-3-030-86855-0_12},

year = 2021,

publisher = {Springer International Publishing},

pages = {169--179},

author = {Vasilii Davydov and Alexey Skrynnik and Konstantin Yakovlev and Aleksandr Panov},

title = {Q-Mixing Network for Multi-agent Pathfinding in Partially Observable Grid Environments},

booktitle = {Artificial Intelligence}

}

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Davydov, Vasilii et al. “Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments.” Lecture Notes in Computer Science (2021): 169–179. Crossref. Web.