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We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 1910
Earlier work this paper cites.
Kinematic state abstraction and provably efficient rich-observation reinforcement learning
Misra, D., Henaff, M., Krishnamurthy, A., and Langford, J · 1911
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Learning multi-domain convolutional neural networks for visual tracking
Nam, H. and Han, B · 2016
Earlier work this paper cites.
Training Region-based Object Detectors with Online Hard Example Mining
Shrivastava, A., Gupta, A., and Girshick, R · 2016
Earlier work this paper cites.
Bootstrapping face detection with hard negative examples, 2016
Wan, S., Chen, Z., Zhang, T., Zhang, B., and kat Wong, K · 2016
Earlier work this paper cites.
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., de Vries, H., Dumoulin, V., and Courville, A. C · 2018
Earlier work this paper cites.
Deepmind control suite, 2018
Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., de Las Casas, D., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., Lillicrap, T., and Riedmiller, M · 2018
Earlier work this paper cites.
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Anand, A., Racah, E., Ozair, S., Bengio, Y., Côté, M.-A., and Hjelm, R. D · 2019
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Language models are unsupervised multitask learners
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Earlier work this paper cites.
Representation learning with contrastive predictive coding, 2019
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Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., 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
Earlier work this paper cites.
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Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., kavukcuoglu, k., Munos, R., and Valko, M · 2020
Earlier work this paper cites.
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Earlier work this paper cites.
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Hard negative mixing for contrastive learning
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Mazoure, B., Tachet des Combes, R., Doan, T. L., Bachman, P., and Hjelm, R. D · 2020
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Cited alongside, same era.
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The unsurprising effectiveness of pre-trained vision models for control
Parisi, S., Rajeswaran, A., Purushwalkam, S., and Gupta, A · 2022
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Masked world models for visual control
Seo, Y., Hafner, D., Liu, H., Liu, F., James, S., Lee, K., and Abbeel, P · 2022
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Transfer RL across observation feature spaces via model-based regularization
Sun, Y., Zheng, R., Wang, X., Cohen, A. E., and Huang, F · 2022
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Hard negative sampling strategies for contrastive representation learning, 2022
Tabassum, A., Wahed, M., Eldardiry, H., and Lourentzou, I · 2022
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Masked visual pre-training for motor control, 2022
Xiao, T., Radosavovic, I., Darrell, T., and Malik, J · 2022
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Mastering visual continuous control: Improved data-augmented reinforcement learning
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Active contrastive learning of audio-visual video representations
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Majumdar, A., Yadav, K., Arnaud, S., Ma, Y. J., Chen, C., Silwal, S., Jain, A., Berges, V.-P., Abbeel, P., Malik, J., Batra, D., Lin, Y., Maksymets, O., Rajeswaran, A., and Meier, F · 2023
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Hyper-decision transformer for efficient online policy adaptation
Xu, M., Lu, Y., Shen, Y., Zhang, S., Zhao, D., and Gan, C · 2023
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Ace : Off-policy actor-critic with causality-aware entropy regularization, 2024
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Xu, G., Zheng, R., Liang, Y., Wang, X., Yuan, Z., Ji, T., Luo, Y., Liu, X., Yuan, J., Hua, P., Li, S., Ze, Y., III, H. D., Huang, F., and Xu, H · 2024
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PRISE: Learning temporal action abstractions as a sequence compression problem
Zheng, R., Cheng, C.-A., III, H. D., Huang, F., and Kolobov, A · 2024
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