mlsanityData to 7 Oct 2026

Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges

arXiv 2305.10091 ↗First posted 17 May 2023cs.AIcited by 5

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Abstract (arXiv)

Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, current studies and applications need to address its scalability, non-stationarity, and trustworthiness. This paper aims to review methods and applications and point out research trends and visionary prospects for the next decade. First, this paper summarizes the basic methods and application scenarios of MARL. Second, this paper outlines the corresponding research methods and their limitations on safety, robustness, generalization, and ethical constraints that need to be addressed in the practical applications of MARL. In particular, we believe that trustworthy MARL will become a hot research topic in the next decade. In addition, we suggest that considering human interaction is essential for the practical application of MARL in various societies. Therefore, this paper also analyzes the challenges while MARL is applied to human-machine interaction.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 145 of its 316 references that are in the map. First posted on arXiv 17 May 2023.

Builds on

145 of 316 references.

References · 145

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38 more
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Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directionscited by 27
  2. 2024Octopus v4: Graph of language modelscited by 11
  3. 2025Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flowcited by 3
  4. 2025Causal Knowledge Transfer for Multi-Agent Reinforcement Learning in Dynamic Environmentscited by 3
  5. 2024Joint Channel Selection using FedDRL in V2Xcited by 0