The content discusses the challenges of offline learning in zero-sum games and proposes a novel approach, ELA, to estimate exploited levels and improve learning efficiency. It introduces a Partially-trainable-conditioned Variational Recurrent Neural Network (P-VRNN) for unsupervised strategy representation learning and demonstrates its effectiveness through various game examples.
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