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In this paper, we examine the effectiveness of pre-training for visuo-motor control tasks.
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A. K., and Efros, A. A · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M. S., Berg, A. C., and Fei-Fei, L · 2015
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N. M. O., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
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Unsupervised perceptual rewards for imitation learning
Sermanet, P., Xu, K., and Levine, S · 2016
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Asymmetric actor critic for image-based robot learning
Pinto, L., Andrychowicz, M., Welinder, P., Zaremba, W., and Abbeel, P · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P · 2017
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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., and Levine, S · 2018
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Deepmind control suite
Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., de Las Casas, D., Budden, D., Abdolmaleki, A., et al · 2018
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Representation learning with contrastive predictive coding
van den Oord, A., Li, Y., and Vinyals, O · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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A simple randomization technique for generalization in deep reinforcement learning
Lee, K., Lee, K., Shin, J., and Lee, H · 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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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T. J., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. B · 2020
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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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Automatic data augmentation for generalization in deep reinforcement learning
Raileanu, R., Goldstein, M., Yarats, D., Kostrikov, I., and Fergus, R · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Srinivas, A., Laskin, M., and Abbeel, P · 2020
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When vision transformers outperform resnets without pretraining or strong data augmentations
Chen, X., Hsieh, C.-J., and Gong, B · 2021
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
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Rt-1: Robotics transformer for real-world control at scale
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., Ibarz, J., Ichter, B., Irpan, A., Jackson, T., Jesmonth, S., Joshi, N., Julian, R., Kalashnikov, D., Kuang, Y., Leal, I., Lee, K.-H., Levine, S., Lu, Y., Malla, U., Manjunath, D., Mordatch, I., Nachum, O., Parada, C., Peralta, J., Perez, E., Pertsch, K., Quiambao, J., Rao, K., Ryoo, M., Salazar, G., Sanketi, P., Sayed, K., Singh, J., Sontakke, S., Stone, A., Tan, C., Tran, H., Vanhoucke, V., Vega, S., Vuong, Q., Xia, F., Xiao, T., Xu, P., Xu, S., Yu, T., and Zitkovich, B · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2022
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Ego4d: Around the world in 3,000 hours of egocentric video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al · 2022
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Generalization in reinforcement learning by soft data augmentation
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Kostrikov, I., Yarats, D., and Fergus, R · 2021
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Learning transferable visual models from natural language supervision
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Data-efficient reinforcement learning with self-predictive representations
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Rrl: Resnet as representation for reinforcement learning
Shah, R. and Kumar, V · 2021
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Masked autoencoders are scalable vision learners
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A comprehensive survey of data augmentation in visual reinforcement learning
Ma, G., Wang, Z., Yuan, Z., Wang, X., Yuan, B., and Tao, D · 2022
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The unsurprising effectiveness of pre-trained vision models for control
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Vrl3: A data-driven framework for visual deep reinforcement learning
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Masked visual pre-training for motor control
Xiao, T., Radosavovic, I., Darrell, T., and Malik, J · 2022
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On the feasibility of cross-task transfer with model-based reinforcement learning
Xu, Y., Hansen, N., Wang, Z., Chan, Y.-C., Su, H., and Tu, Z · 2022
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Pre-trained image encoder for generalizable visual reinforcement learning
Yuan, Z., Xue, Z., Yuan, B., Wang, X., Wu, Y., Gao, Y., and Xu, H · 2022
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Visual reinforcement learning with self-supervised 3d representations
Ze, Y., Hansen, N., Chen, Y., Jain, M., and Wang, X · 2022
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Stanford alpaca: An instruction-following llama model
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