Kaelbling, L. P., Littman, M. L., and Cassandra, A. R. Planning and Acting in Partially Observable Stochastic Domains. Artificial Intelligence, 101:99-134, 1998.
Kassner, N., Krojer, B., and Schütze, H. Are Pretrained Language Models Symbolic Reasoners over Knowledge? In Fernández, R. and Linzen, T. (eds.), Proceedings of the 24th Conference on Computational Natural Language Learning, CoNLL 2020, Online, November 19-20, 2020, pp. 552-564. Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020.conll-1.45.
Khetarpal, K., Klissarov, M., Chevalier-Boisvert, M., Bacon, P., and Precup, D. Options of interest: Temporal abstraction with interest functions. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 4444-4451. AAAI Press, 2020.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N. C., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R. Overcoming catastrophic forgetting in neural networks. CoRR, abs/1612.00796, 2016.
Kwon, M., Xie, S. M., Bullard, K., and Sadigh, D. Reward design with language models. In The Eleventh International Conference on Learning Representations, 2023.
Leibo, J. Z., de Masson d'Autume, C., Zoran, D., Amos, D., Beattie, C., Anderson, K., Castañeda, A. G., Sanchez, M., Green, S., Gruslys, A., Legg, S., Hassabis, D., and Botvinick, M. M. Psychlab: A psychology laboratory for deep reinforcement learning agents. CoRR, abs/1801.08116, 2018.
Li, J., Li, D., Savarese, S., and Hoi, S. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023.
Li, S., Puig, X., Paxton, C., Du, Y., Wang, C., Fan, L., Chen, T., Huang, D.-A., Akyurek, E., Anandkumar, A., Andreas, J., Mordatch, I., Torralba, A., and Zhu, Y. Pre-Trained Language Models for Interactive Decision-Making. CoRR, abs/2202.01771, 2022. arXiv: 2202.01771.
Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., and Zeng, A. Code as policies: Language model programs for embodied control. CoRR, abs/2209.07753, 2022a. doi: 10.48550/arXiv.2209.07753.
Liang, W., Zhang, Y., Kwon, Y., Yeung, S., and Zou, J. Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K. (eds.), Advances in Neural Information Processing Systems, 2022b.
Liu, E. Z., Raghunathan, A., Liang, P., and Finn, C. Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices. In Meila, M. and Zhang, T. (eds.), Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pp. 6925-6935. PMLR, 2021.
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., and Fedorenko, E. Dissociating language and thought in large language models: a cognitive perspective, 2023.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M. A., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. Human-level control through deep reinforcement learning. Nat., 518(7540):529-533, 2015. doi: 10.1038/nature14236.
Mu, J., Zhong, V., Raileanu, R., Jiang, M., Goodman, N. D., Rocktäschel, T., and Grefenstette, E. Improving Intrinsic Exploration with Language Abstractions. CoRR, abs/2202.08938, 2022. arXiv: 2202.08938.
Narang, S., Raffel, C., Lee, K., Roberts, A., Fiedel, N., and Malkan, K. Wt5?! training text-to-text models to explain their predictions. CoRR, abs/2004.14546, 2020.
Ostapenko, O., Lesort, T., Rodríguez, P., Arefin, M. R., Douillard, A., Rish, I., and Charlin, L. Foundational Models for Continual Learning: An Empirical Study of Latent Replay. arXiv preprint arXiv:2205.00329, 2022.