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This paper introduces a meta-multi-agent reinforcement learning (meta-MARL) framework that formulates MARL problems as Markov games and defines a new solution concept, meta-NE, with proven equivalence to stationary points of a gradient-play-based algorithm, demonstrating faster adaptation on autonomous-driving tasks compared to pretrained MARL baselines.