MADIFF: OfflineMulti-agentLearning withDiffusionModels

Offline reinforcement learning (RL) aims to learn policies from pre-existing datasets without further interactions, making it a challenging task. Q-learning algorithms struggle withextrapolation errors inofflinesettings, while supervised learning methods are constrained by model expressiveness.