Core Concepts
The author introduces ELA to estimate exploited levels in zero-sum games, enhancing offline learning algorithms significantly.
Abstract
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.
Stats
"Our method enables interpretable exploited level estimation in multiple zero-sum games."
"ELA significantly enhances both imitation and offline reinforcement learning performance."
"EL(τk) = 1/6."
"E(π(τ)) = 2/3."
"EL is an appropriate indicator."