Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey

• URL: http://arxiv.org/abs/2401.04934v1
• Date: Wed, 10 Jan 2024 05:07:42 GMT
• Title: Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
• Authors: Jiechuan Jiang, Kefan Su, Zongqing Lu
• Abstract summary: Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks. It is challenging to derive algorithms that can converge to the optimal joint policy in a fully decentralized setting.
• Score: 48.77342627610471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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