Jiang, Y., J. Z. Kolter, and R. Raileanu (2022). Uncertainty-driven exploration for generalization in reinforcement learning. In Deep Reinforcement Learning Workshop NeurIPS 2022.
Kojima, T., S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa (2022). Large language models are zero-shot reasoners. arXiv preprint arXiv:2205.11916.
Kolve, E., R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, D. Gordon, Y. Zhu, A. Gupta, and A. Farhadi (2017). AI2-THOR: An Interactive 3D Environment for Visual AI. arXiv.
Liu, S., Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, et al. (2023). Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499.
Madaan, A., N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, et al. (2023). Self-refine: Iterative refinement with self-feedback. arXiv preprint arXiv:2303.17651.
Manyika, J. An overview of bard: an early experiment with generative ai. https://ai.google/static/documents/google-about-bard.pdf. Accessed: May 27, 2023.
OpenAI. Gpt-3.5. https://platform.openai.com/docs/models/gpt-3-5. Accessed: May 27, 2023.
OpenAI (2023). Gpt-4 technical report.
Ouyang, L., J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730-27744.
Schrittwieser, J., I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, et al. (2020). Mastering atari, go, chess and shogi by planning with a learned model. Nature 588(7839), 604-609.
Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
Sekar, R., O. Rybkin, K. Daniilidis, P. Abbeel, D. Hafner, and D. Pathak (2020). Planning to explore via self-supervised world models. In International Conference on Machine Learning, pp. 8583-8592. PMLR.
Shinn, N., B. Labash, and A. Gopinath (2023). Reflection: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366.
Shridhar, M., X. Yuan, M.-A. Côté, Y. Bisk, A. Trischler, and M. Hausknecht (2020). Alf-world: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768.
Shu, T., C. Xiong, and R. Socher (2017). Hierarchical and interpretable skill acquisition in multi-task reinforcement learning. arXiv preprint arXiv:1712.07294.
Silver, D., J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al. (2017). Mastering the game of go without human knowledge. nature 550(7676), 354-359.
Smith, S., M. Patwary, B. Norick, P. LeGresley, S. Rajbhandari, J. Casper, Z. Liu, S. Prabhumoye, G. Zerveas, V. Korthikanti, E. Zheng, R. Child, R. Y. Aminabadi, J. Bernauer, X. Song, M. Shoeybi, Y. He, M. Houston, S. Tiwary, and B. Catanzaro (2022). Using deepspeed and megatron to train megatron-turing NLG 530b, A large-scale generative language model. CoRR abs/2201.11990.
Song, C. H., J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su (2022). Llm-planner: Few-shot grounded planning for embodied agents with large language models. arXiv preprint arXiv:2212.04088.