Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey

Jiechuan Jiang; Kefan Su; Zongqing Lu

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arXiv · 2401.04934

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey

Jiechuan Jiang·Kefan Su·Zongqing Lu

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Abstract

Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner. Due to the lack of information about other agents, it is challenging to derive algorithms that can converge to the optimal joint policy in a fully decentralized setting. Thus, this research area has not been thoroughly studied. In this paper, we seek to systematically review the fully decentralized methods in two settings: maximizing a shared reward of all agents and maximizing the sum of individual rewards of all agents, and discuss open questions and future research directions.

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Jiechuan Jiang, Kefan Su, Zongqing Lu. 2024-01-10. Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey. https://arxiv.org/abs/2401.04934

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