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arXiv · 2010.01207

$f$-GAIL: Learning $f$-Divergence for Generative Adversarial Imitation Learning

Xin Zhang·Yanhua Li·Ziming Zhang·Zhi-Li Zhang
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Abstract

Imitation learning (IL) aims to learn a policy from expert demonstrations that minimizes the discrepancy between the learner and expert behaviors. Various imitation learning algorithms have been proposed with different pre-determined divergences to quantify the discrepancy. This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency? In this work, we propose $f$-GAIL, a new generative adversarial imitation learning (GAIL) model, that automatically learns a discrepancy measure from the $f$-divergence family as well as a policy capable of producing expert-like behaviors. Compared with IL baselines with various predefined divergence measures, $f$-GAIL learns better policies with higher data efficiency in six physics-based control tasks.

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BibTeXRIS

Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang. 2020-10-02. $f$-GAIL: Learning $f$-Divergence for Generative Adversarial Imitation Learning. https://arxiv.org/abs/2010.01207

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