Visual Transfer For Reinforcement Learning Via Wasserstein Domain Confusion

No. 11: AAAI-21 Technical Tracks 11

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Machine Learning IV

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.

10.1609/aaai.v35i11.17139