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We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning.
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Human-level control through deep reinforcement learning
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A distributional perspective on reinforcement learning
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Unsupervised control through non-parametric discriminative rewards
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Unsupervised state representation learning in atari
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Randaugment: Practical automated data augmentation with a reduced search space, 2019
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Darla: Improving zero-shot transfer in reinforcement learning
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
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Ha, D. and Schmidhuber, J · 2018
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Soft actor-critic algorithms and applications
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Dream to control: Learning behaviors by latent imagination
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Data-efficient image recognition with contrastive predictive coding
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Model-based reinforcement learning for atari
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Mastering atari, go, chess and shogi by planning with a learned model
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On mutual information maximization for representation learning
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When to use parametric models in reinforcement learning?
van Hasselt, H. P., Hessel, M., and Aslanides, J · 2019
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Improving sample efficiency in model-free reinforcement learning from images
Yarats, D., Zhang, A., Kostrikov, I., Amos, B., Pineau, J., and Fergus, R · 2019
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A simple framework for contrastive learning of visual representations, 2020
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Do recent advancements in model-based deep reinforcement learning really improve data efficiency?, 2020
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
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Reinforcement learning with augmented data
Laskin, M., Lee, K., Stooke, A., Pinto, L., Abbeel, P., and Srinivas, A · 2020
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