Badia, A. P., B. Piot, S. Kapturowski, P. Sprechmann, A. Vitvitskyi, Z. D. Guo, and C. Blundell (2020). Agent57: Outperforming the atari human benchmark. In International Conference on Machine Learning, pp. 507-517. PMLR.
Branavan, S., D. Silver, and R. Barzilay (2012). Learning to win by reading manuals in a montecarlo framework. Journal of Artificial Intelligence Research 43, 661-704.
Brown, T. B., B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
Bubeck, S., V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al. (2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.
Burda, Y., H. Edwards, A. Storkey, and O. Klimov (2018). Exploration by random network distillation. arXiv preprint arXiv:1810.12894.
Chowdhery, A., S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al. (2022). Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311.
Driess, D., F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Thompson, Q. Vuong, T. Yu, et al. (2023). Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378.
Du, Y., O. Watkins, Z. Wang, C. Colas, T. Darrell, P. Abbeel, A. Gupta, and J. Andreas (2023). Guiding pretraining in reinforcement learning with large language models. arXiv preprint arXiv:2302.06692.
Fan, L., G. Wang, Y. Jiang, A. Mandlekar, Y. Yang, H. Zhu, A. Tang, D.-A. Huang, Y. Zhu, and A. Anandkumar (2022). Minedojo: Building open-ended embodied agents with internet-scale knowledge. arXiv preprint arXiv:2206.08853.
Fu, J., A. Kumar, O. Nachum, G. Tucker, and S. Levine (2020). D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219.
Goldwasser, D. and D. Roth (2014). Learning from natural instructions. Machine learning 94, 205-232.
Goyal, P., S. Niekum, and R. J. Mooney (2019). Using natural language for reward shaping in reinforcement learning. arXiv preprint arXiv:1903.02020.
Guss, W. H., M. Y. Castro, S. Devlin, B. Houghton, N. S. Kuno, C. Loomis, S. Milani, S. Mohanty, K. Nakata, R. Salakhutdinov, et al. (2021). The minerl 2020 competition on sample efficient reinforcement learning using human priors. arXiv preprint arXiv:2101.11071.
Hafner, D. (2021). Benchmarking the spectrum of agent capabilities. arXiv preprint arXiv:2109.06780.
Hafner, D., T. Lillicrap, M. Norouzi, and J. Ba (2020). Mastering atari with discrete world models. arXiv preprint arXiv:2010.02193.
Hafner, D., J. Pasukonis, J. Ba, and T. Lillicrap (2023). Mastering diverse domains through world models. arXiv preprint arXiv:2301.04104.
He, K., G. Gkioxari, P. Dollar, and R. Girshick (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969.
Hessel, M., J. Modayil, H. Van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver (2018). Rainbow: Combining improvements in deep reinforcement learning. In Thirty-second AAAI conference on artificial intelligence.
Huang, W., P. Abbeel, D. Pathak, and I. Mordatch (2022). Language models as zero-shot planners: Extracting actionable knowledge for embodied agents.