The DOI matches its Crossref record; 197 of the 197 references it cites were found in a registry or an index.
A review of cooperative multi-agent deep reinforcement learning
Abstract
Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. The aim of this review article is to provide an overview of recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. Our classification of MARL approaches includes five categories for modeling and solving cooperative multi-agent reinforcement learning problems: (I) independent learners, (II) fully observable critics, (III) value function factorization, (IV) consensus, and (IV) learn to communicate. We first discuss each of these methods, their potential challenges, and how these challenges were mitigated in the relevant papers. Additionally, we make connections among different papers in each category if applicable. Next, we cover some new emerging research areas in MARL along with the relevant recent papers. In light of MARL’s recent success in real-world applications, we have dedicated a section to reviewing these applications and articles. This survey also provides a list of available environments for MARL research. Finally, the paper is concluded with proposals on possible research directions.
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Gronauer, S., & Diepold, K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-09996-w · Cited by 957
Xie, J., Ajagekar, A., & You, F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121162 · Cited by 109
Collective intelligence for deep learning: A survey of recent developments
Ha, D., & Tang, Y. (2022) · Collective Intelligence · doi:10.1177/26339137221114874 · Cited by 71
Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey
Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35
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Metadata
- Authors
- Oroojlooy, A., & Hajinezhad, D.
- Venue
- Applied Intelligence
- Published
- 2022-10-14
- Volume
- 53
- Issue
- 11
- Pages
- 13677-13722
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- OpenAlex
- W2968526727
- Cited by
- 482
- References
- 197
- FWCI
- 29.08
- Type
- journal article
- Language
- en
- Open access
- closed
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