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Large transformer models powered by diverse data and model scale have dominated natural language modeling and computer vision and pushed the frontier of multiple AI areas.
Learning from delayed rewards
Watkins, C · 1989
Earlier work this paper cites.
Learning to achieve goals
Kaelbling, L. P · 1993
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
Earlier work this paper cites.
Universal value function approximators
Schaul, T., Horgan, D., Gregor, K., and Silver, D · 2015
Earlier work this paper cites.
Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M. I., and Moritz, P · 2015
Earlier work this paper cites.
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Earlier work this paper cites.
Rl2: Fast reinforcement learning via slow reinforcement learning
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P · 2016
Earlier work this paper cites.
Deep reinforcement learning with double q-learning
Van Hasselt, H., Guez, A., and Silver, D · 2016
Earlier work this paper cites.
Learning to reinforcement learn
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M · 2016
Earlier work this paper cites.
Hindsight experience replay
Andrychowicz, M., Crow, D., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, P., and Zaremba, W · 2017
Earlier work this paper cites.
Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Fujimoto, S., van Hoof, H., and Meger, D · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
Earlier work this paper cites.
Exploration by random network distillation
Burda, Y., Edwards, H., Storkey, A. J., and Klimov, O · 2019
Earlier work this paper cites.
Diversity is all you need: Learning skills without a reward function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S · 2019
Cited alongside, same era.
Efficient exploration via state marginal matching
Lee, L., Eysenbach, B., Parisotto, E., Xing, E., Levine, S., and Salakhutdinov, R · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
Cited alongside, same era.
Self-supervised exploration via disagreement
Pathak, D., Gandhi, D., and Gupta, A · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
Cited alongside, same era.
From play to policy: Conditional behavior generation from uncurated robot data
Cui, Z. J., Wang, Y., Muhammad, N., Pinto, L., et al · 2022
Later among the works it cites.
Instruction-driven history-aware policies for robotic manipulations
Guhur, P.-L., Chen, S., Garcia, R., Tapaswi, M., Laptev, I., and Schmid, C · 2022
Later among the works it cites.
Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., de Las Casas, D., Hendricks, L. A., Welbl, J., Clark, A., Hennigan, T., Noland, E., Millican, K., van den Driessche, G., Damoc, B., Guy, A., Osindero, S., Simonyan, K., Elsen, E., Rae, J. W., Vinyals, O., and Sifre, L · 2022
Later among the works it cites.
In-context reinforcement learning with algorithm distillation
Laskin, M., Wang, L., Oh, J., Parisotto, E., Spencer, S., Steigerwald, R., Strouse, D., Hansen, S., Filos, A., Brooks, E., et al · 2022
Later among the works it cites.
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Srivastava, R. K., Shyam, P., Mutz, F., Jaśkowski, W., and Schmidhuber, J · 2019
Cited alongside, same era.
What matters in on-policy reinforcement learning? a large-scale empirical study
Andrychowicz, M., Raichuk, A., Stańczyk, P., Orsini, M., Girgin, S., Marinier, R., Hussenot, L., Geist, M., Pietquin, O., Michalski, M., Gelly, S., and Bachem, O · 2020
Cited alongside, same era.
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., et al · 2020
Cited alongside, same era.
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., et al · 2020
Cited alongside, same era.
D4rl: Datasets for deep data-driven reinforcement learning
Fu, J., Kumar, A., Nachum, O., Tucker, G., and Levine, S · 2020
Cited alongside, same era.
Planning from pixels using inverse dynamics models
Paster, K., McIlraith, S. A., and Ba, J · 2020
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Fujimoto, S. and Gu, S. S · 2021
Cited alongside, same era.
Lee, K.-H., Nachum, O., Yang, M., Lee, L., Freeman, D., Xu, W., Guadarrama, S., Fischer, I., Jang, E., Michalewski, H., et al · 2022
Later among the works it cites.
Solving quantitative reasoning problems with language models
Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al · 2022
Later among the works it cites.
Transformers are meta-reinforcement learners
Melo, L. C · 2022
Later among the works it cites.
Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., et al · 2022
Later among the works it cites.
Masked world models for visual control
Seo, Y., Hafner, D., Liu, H., Liu, F., James, S., Lee, K., and Abbeel, P · 2022
Later among the works it cites.
Behavior transformers: Cloning k k modes with one stone
Shafiullah, N. M. M., Cui, Z. J., Altanzaya, A., and Pinto, L · 2022
Later among the works it cites.
Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
Shah, D., Osinski, B., Ichter, B., and Levine, S · 2022
Later among the works it cites.
Cliport: What and where pathways for robotic manipulation
Shridhar, M., Manuelli, L., and Fox, D · 2022
Later among the works it cites.
Don’t change the algorithm, change the data: Exploratory data for offline reinforcement learning
Yarats, D., Brandfonbrener, D., Liu, H., Laskin, M., Abbeel, P., Lazaric, A., and Pinto, L · 2022
Later among the works it cites.
Vlmbench: A compositional benchmark for vision-and-language manipulation
Zheng, K., Chen, X., Jenkins, O. C., and Wang, X · 2022
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Chain of hindsight aligns language models with feedback
Liu, H., Sferrazza, C., and Abbeel, P · 2023
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Human-timescale adaptation in an open-ended task space
Team, A. A., Bauer, J., Baumli, K., Baveja, S., Behbahani, F., Bhoopchand, A., Bradley-Schmieg, N., Chang, M., Clay, N., Collister, A., et al · 2023
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Masked trajectory models for prediction, representation, and control
Wu, P., Majumdar, A., Stone, K., Lin, Y., Mordatch, I., Abbeel, P., and Rajeswaran, A · 2023
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