M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman, A. Herzog, D. Ho, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, E. Jang, R. J. Ruano, K. Jeffrey, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, K.-H. Lee, S. Levine, Y. Lu, L. Liu, C. Parada, P. Pastor, J. Quiambao, K. Rao, J. Rettinghouse, D. Reyes, P. Sermanet, N. Sievers, C. Tan, A. Toshev, V. Vanhoucke, F. Xia, T. Xiao, P. Xu, S. Xu, M. Yan, and A. Zeng. Do as i can and not as i say: Grounding language in robotic affordances. In arXiv preprint arXiv:2204.01691, 2022.
A. Ajay, Y. Du, A. Gupta, J. Tenenbaum, T. Jaakkola, and P. Agrawal. Is conditional generative modeling all you need for decision-making? arXiv preprint arXiv:2211.15657, 2022.
J. Andreas, D. Klein, and S. Levine. Modular multitask reinforcement learning with policy sketches. In International Conference on Machine Learning, pages 166-175. PMLR, 2017.
H. Chen, C. Lu, C. Ying, H. Su, and J. Zhu. Offline reinforcement learning via high-fidelity generative behavior modeling. arXiv preprint arXiv:2209.14548, 2022.
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch. Decision transformer: Reinforcement learning via sequence modeling. Advances in neural information processing systems, 34:15084-15097, 2021.
Y. Dai, M. Yang, B. Dai, H. Dai, O. Nachum, J. Tenenbaum, D. Schuurmans, and P. Abbeel. Learning universal policies via text-guided video generation. arXiv preprint arXiv:2302.00111, 2023.
S. Emmons, B. Eysenbach, I. Kostrikov, and S. Levine. Rvs: What is essential for offline rl via supervised learning? arXiv preprint arXiv:2112.10751, 2021.
J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine. D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219, 2020.
S. Fujimoto and S. S. Gu. A minimalist approach to offline reinforcement learning. Advances in neural information processing systems, 34:20132-20145, 2021.
S. Fujimoto, D. Meger, and D. Precup. Off-policy deep reinforcement learning without exploration. In International conference on machine learning, pages 2052-2062. PMLR, 2019.
S. K. S. Ghasemipour, D. Schuurmans, and S. S. Gu. Emaq: Expected-max q-learning operator for simple yet effective offline and online rl. In International Conference on Machine Learning, pages 3682-3691. PMLR, 2021.
J. Ho and T. Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020.
M. Janner, Q. Li, and S. Levine. Offline reinforcement learning as one big sequence modeling problem. Advances in neural information processing systems, 34:1273-1286, 2021.
M. Janner, Y. Du, J. B. Tenenbaum, and S. Levine. Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991, 2022.
L. P. Kaelbling, M. L. Littman, and A. R. Cassandra. Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2):99-134, 1998.
R. Kidambi, A. Rajeswaran, P. Netrapalli, and T. Joachims. Morel: Model-based offline reinforcement learning. Advances in neural information processing systems, 33:21810-21823, 2020.
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollar, and R. Girshick. Segment anything. arXiv:2304.02643, 2023.