Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
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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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Cited by 2 Pith papers
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COOPER is a distributed MARL method that learns emergent reputation assessment rules and policies from rewards, shown on donation and coin games in grid worlds with adaptation across co-players and networks.
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Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem
DAC models fully decentralized cooperative MARL as a context modeling problem, using latent variables for joint policies to fix non-stationarity in value updates and relative overgeneralization in value estimation.
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