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The success of transformer models trained with a language modeling objective brings a promising opportunity to the reinforcement learning framework.
Approximating optimal policies for partially observable stochastic domains
Parr, R. and Russell, S · 1995
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Learning inverse dynamics: a comparison
Nguyen-Tuong, D., Peters, J., Seeger, M., and Schölkopf, B · 2008
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Babyai: A platform to study the sample efficiency of grounded language learning
Chevalier-Boisvert, M., Bahdanau, D., Lahlou, S., Willems, L., Saharia, C., Nguyen, T. H., and Bengio, Y · 2018
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Diversity is all you need: Learning skills without a reward function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S · 2018
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 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., 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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D4rl: Datasets for deep data-driven reinforcement learning
Fu, J., Kumar, A., Nachum, O., Tucker, G., and Levine, S · 2020
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Rl unplugged: A suite of benchmarks for offline reinforcement learning
Gulcehre, C., Wang, Z., Novikov, A., Paine, T., Gómez, S., Zolna, K., Agarwal, R., Merel, J. S., Mankowitz, D. J., Paduraru, C., et al · 2020
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Human instruction-following with deep reinforcement learning via transfer-learning from text
Hill, F., Mokra, S., Wong, N., and Harley, T · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Levine, S., Kumar, A., Tucker, G., and Fu, J · 2020
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Improving vision-and-language navigation with image-text pairs from the web
Majumdar, A., Shrivastava, A., Lee, S., Anderson, P., Parikh, D., and Batra, D · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2020
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Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
Video pretraining (vpt): Learning to act by watching unlabeled online videos
Baker, B., Akkaya, I., Zhokhov, P., Huizinga, J., Tang, J., Ecoffet, A., Houghton, B., Sampedro, R., and Clune, J · 2022
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Hierarchical decision transformer
Correia, A. and Alexandre, L. A · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
Fan, L., Wang, G., Jiang, Y., Mandlekar, A., Yang, Y., Zhu, H., Tang, A., Huang, D.-A., Zhu, Y., and Anandkumar, A · 2022
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The challenges of exploration for offline reinforcement learning
Lambert, N., Wulfmeier, M., Whitney, W., Byravan, A., Bloesch, M., Dasagi, V., Hertweck, T., and Riedmiller, M · 2022
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Cited alongside, same era.
Offline reinforcement learning as one big sequence modeling problem
Janner, M., Li, Q., and Levine, S · 2021
Cited alongside, same era.
Pretraining for language conditioned imitation with transformers
Putterman, A. L., Lu, K., Mordatch, I., and Abbeel, P · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Reinforcement learning with prototypical representations
Yarats, D., Fergus, R., Lazaric, A., and Pinto, L · 2021
Cited alongside, same era.
Li, S., Puig, X., Du, Y., Wang, C., Akyurek, E., Torralba, A., Andreas, J., and Mordatch, I · 2022
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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
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Sprint: Scalable semantic policy pre-training via language instruction relabeling
Zhang, J., Pertsch, K., Zhang, J., Nam, T., Hwang, S. J., Ren, X., and Lim, J. J · 2022
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Grounding large language models in interactive environments with online reinforcement learning
Carta, T., Romac, C., Wolf, T., Lamprier, S., Sigaud, O., and Oudeyer, P.-Y · 2023
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A survey on transformers in reinforcement learning
Li, W., Luo, H., Lin, Z., Zhang, C., Lu, Z., and Ye, D · 2023
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