Visual Transfer For Reinforcement Learning Via Wasserstein Domain Confusion

Authors

  • Josh Roy Brown University
  • George D. Konidaris Brown University

Keywords:

Reinforcement Learning, Transfer/Adaptation/Multi-task/Meta/Automated Learning

Abstract

We introduce Wasserstein Adversarial Proximal Policy Optimization (WAPPO), a novel algorithm for visual transfer in Reinforcement Learning that explicitly learns to align the distributions of extracted features between a source and target task. WAPPO approximates and minimizes the Wasserstein-1 distance between the distributions of features from source and target domains via a novel Wasserstein Confusion objective. WAPPO outperforms the prior state-of-the-art in visual transfer and successfully transfers policies across Visual Cartpole and both the easy and hard settings of of 16 OpenAI Procgen environments.

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Published

2021-05-18

How to Cite

Roy, J., & Konidaris, G. D. (2021). Visual Transfer For Reinforcement Learning Via Wasserstein Domain Confusion. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 9454-9462. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17139

Issue

Section

AAAI Technical Track on Machine Learning IV