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We study the application of multi-agent reinforcement learning for game-theoretical problems. In particular, we are interested in coalition formation problems and their variants such as hedonic coalition formation games (also called hedonic games), matching (a common type of hedonic game), and coalition formation for task allocation. We consider decentralized multi-agent systems where autonomous agents inhabit an environment without any prior knowledge of other agents or the system. We alsodblp:conf/aaai/Taywade21 fatcat:wgfjgcmcova7fl67wnea53j73u