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Sample-efficient Adversarial Imitation Learning
Dahuin Jung · Hyungyu Lee · Sungroh Yoon

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in Deep Reinforcement Learning Workshop »
Event URL: https://openreview.net/forum?id=hxhoheCtUNn »

Imitation learning, wherein learning is performed by demonstration, has been studied and advanced for sequential decision-making tasks in which a reward function is not predefined. However, imitation learning methods still require numerous expert demonstration samples to successfully imitate an expert's behavior. To improve sample efficiency, we utilize self-supervised representation learning, which can generate vast training signals from the given data. In this study, we propose a self-supervised representation-based adversarial imitation learning method to learn state and action representations that are robust to diverse distortions and temporally predictive, on non-image control tasks. Particularly, in comparison with existing self-supervised learning methods for tabular data, we propose a different corruption method for state and action representations robust to diverse distortions. The proposed method shows a 39% relative improvement over the existing adversarial imitation learning methods on MuJoCo in a setting limited to 100 expert state-action pairs. Moreover, we conduct comprehensive ablations and additional experiments using demonstrations with varying optimality to provide the intuitions of a range of factors.

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Author Information

Dahuin Jung (Seoul National University)
Hyungyu Lee (Seoul National University)
Sungroh Yoon (Seoul National University)

Dr. Sungroh Yoon is Associate Professor of Electrical and Computer Engineering at Seoul National University, Korea. Prof. Yoon received the B.S. degree from Seoul National University, South Korea, and the M.S. and Ph.D. degrees from Stanford University, CA, respectively, all in electrical engineering. He held research positions with Stanford University, CA, Intel Corporation, Santa Clara, CA, and Synopsys, Inc., Mountain View, CA. He was an Assistant Professor with the School of Electrical Engineering, Korea University, from 2007 to 2012. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, Seoul National University, South Korea. Prof. Yoon is the recipient of 2013 IEEE/IEIE Joint Award for Young IT Engineers. His research interests include deep learning, machine learning, data-driven artificial intelligence, and large-scale applications including biomedicine.

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