[1] Achiam, J., Held, D., Tamar, A., and Abbeel, P. Constrained policy optimization. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 22–31. JMLR.org, 2017.
[2] Agarwal, R., Schuurmans, D., and Norouzi, M. An optimistic perspective on offline reinforcement learning. In International Conference on Machine Learning (ICML), 2020.
[3] An, G., Moon, S., Kim, J.-H., and Song, H. O. Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble. arXiv e-prints, art. arXiv:2110.01548, October 2021.
[4] Argenson, A. and Dulac-Arnold, G. Model-based offline planning. arXiv preprint arXiv:2008.05556, 2020.
[5] Brandfonbrener, D., Whitney, W. F., Ranganath, R., and Bruna, J. Offline RL without off-policy evaluation. CoRR, abs/2106.08909, 2021. URL https://arxiv.org/abs/2106.08909.
[6] Cao, K., Chen, Y., Lu, J., Arechiga, N., Gaidon, A., and Ma, T. Heteroskedastic and imbalanced deep learning with adaptive regularization. arXiv preprint arXiv:2006.15766, 2020.
[7] Castro, P. S., Moitra, S., Gelada, C., Kumar, S., and Bellemare, M. G. Dopamine: A Research Framework for Deep Reinforcement Learning. 2018. URL http://arxiv.org/abs/1812.06110.
[8] Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I. Decision transformer: Reinforcement learning via sequence modeling. arXiv preprint arXiv:2106.01345, 2021.
[9] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255. IEEE, 2009.
[10] Ebert, F., Yang, Y., Schmeckpeper, K., Bucher, B., Georgakis, G., Daniilidis, K., Finn, C., and Levine, S. Bridge data: Boosting generalization of robotic skills with cross-domain datasets. arXiv preprint arXiv:2109.13396, 2021.
[11] Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C. R., Zhou, Y., et al. Large scale interactive motion forecasting for autonomous driving: The way to open motion dataset. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9710-9719, 2021.
[12] Fu, J., Kumar, A., Soh, M., and Levine, S. Diagnosing bottlenecks in deep Q-learning algorithms. arXiv preprint arXiv:1902.10250, 2019.
[13] Fu, J., Kumar, A., Nachum, O., Tucker, G., and Levine, S. D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219, 2020.
[14] Fu, J., Norouzi, M., Nachum, O., Tucker, G., ziyu wang, Novikov, A., Yang, M., Zhang, M. R., Chen, Y., Kumar, A., Paduraru, C., Levine, S., and Paine, T. Benchmarks for deep off-policy evaluation. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=kWSeGEeHvF8.
[15] Fujimoto, S. and Gu, S. S. A minimalist approach to offline reinforcement learning. arXiv preprint arXiv:2106.06860, 2021.
[16] Fujimoto, S. and Gu, S. S. A minimalist approach to offline reinforcement learning. CoRR, abs/2106.06860, 2021. URL https://arxiv.org/abs/2106.06860.
[17] Fujimoto, S., Meger, D., and Precup, D. Off-policy deep reinforcement learning without exploration. arXiv preprint arXiv:1812.02900, 2018.
[18] Fujimoto, S., van Hoof, H., and Meger, D. Addressing function approximation error in actor-critic methods. In International Conference on Machine Learning (ICML), pp. 1587-1596, 2018.
[19] Garcia, J. and Fernández, F. A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research, 16(1):1437-1480, 2015.
[20] Ghasemipour, S. K. S., Schuurmans, D., and Gu, S. S. Emaq: Expected-max q-learning operator for simple yet effective offline and online rl. In International Conference on Machine Learning, pp. 3682-3691. PMLR, 2021.