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Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion
[article]
2020
arXiv
pre-print
Preserved Fulltext
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
arXiv:2006.03465v1
fatcat:yfobpv4tdrhp5njilmgygf5qnu