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We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training.
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Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Van Hasselt, Marc Lanctot, and Nando De Freitas · 2015
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Layer normalization
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Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2017
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2019
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Data-efficient image recognition with contrastive predictive coding
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Manifold regularization for kernelized lstd
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Understanding the impact of entropy on policy optimization
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Regularization matters in policy optimization
Zhuang Liu, Xuanlin Li, Bingyi Kang, and Trevor Darrell · 2019
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Robust visual domain randomization for reinforcement learning
Reda Bahi Slaoui, William R. Clements, Jakob N. Foerster, and Sébastien Toth · 2019
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Hado van Hasselt, Matteo Hessel, and John Aslanides · 2019
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When to use parametric models in reinforcement learning?
Hado P van Hasselt, Matteo Hessel, and John Aslanides · 2019
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Do recent advancements in model-based deep reinforcement learning really improve data efficiency?
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