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For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance.
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Noisy networks for exploration
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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Attention is all you need
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Rainbow: Combining improvements in deep reinforcement learning
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Learning latent dynamics for planning from pixels
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Deep reinforcement learning at the edge of the statistical precipice
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Beit: Bert pre-training of image transformers
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Generalization in reinforcement learning by soft data augmentation
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Mastering atari games with limited data
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Playvirtual: Augmenting cycle-consistent virtual trajectories for reinforcement learning
Yu, T., Lan, C., Zeng, W., Feng, M., Zhang, Z., and Chen, Z · 2021
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Learning invariant representations for reinforcement learning without reconstruction
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Byol-explore: Exploration by bootstrapped prediction
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Masked feature prediction for self-supervised visual pre-training
Wei, C., Fan, H., Xie, S., Wu, C.-Y., Yuille, A., and Feichtenhofer, C · 2022
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Simmim: A simple framework for masked image modeling
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