Generative adversarial imitation learning with neural networks: Global optimality and convergence rate

Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks. Different from reinforcement learning, GAIL learns both policy and reward function from expert (human) demonstration. Despite its empirical success, it remains unclear whether GAIL with neural networks converges to the globally optimal solution. The major difficulty comes from the nonconvex-nonconcave minimax optimization structure. To bridge the gap between practice and theory, we analyze a gradient-based algorithm with alternating updates and establish its sublinear convergence to the globally optimal solution. To the best of our knowledge, our analysis establishes the global optimality and convergence rate of GAIL with neural networks for the first time.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Mar 7 2020

ASJC Scopus subject areas

  • General

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