[6] Mohammad Gheshlaghi Azar, Rémi Munos, and Bert Kappen. On the sample complexity of reinforcement learning with a generative model. arXiv preprint arXiv:1206.6461, 2012.
[7] Shekoofeh Azizi, Simon Kornblith, Chitwan Sahara, Mohammad Norouzi, and David J. Fleet. Synthetic data from diffusion models improves imagenet classification, 2023.
[8] Philip J Ball, Cong Lu, Jack Parker-Holder, and Stephen Roberts. Augmented world models facilitate zero-shot dynamics generalization from a single offline environment. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 619-629. PMLR, 18-24 Jul 2021. URL https://proceedings.mlr.press/v139/ba121a.html.
[9] Philip J. Ball, Laura Smith, Ilya Kostrikov, and Sergey Levine. Efficient online reinforcement learning with offline data, 2023. URL https://arxiv.org/abs/2302.02948.
[10] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym, 2016.
[11] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
[12] Xinyue Chen, Che Wang, Zijian Zhou, and Keith W. Ross. Randomized ensembled double q-learning: Learning fast without a model. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=AY8zfZmOtDd.
[13] Zoey Chen, Sho Kiami, Abhishek Gupta, and Vikash Kumar. Genaug: Retargeting behaviors to unseen situations via generative augmentation, 2023. URL https://arxiv.org/abs/2302.06671.
[14] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[15] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[16] Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, and Aaron Courville. Sample-efficient reinforcement learning by breaking the replay ratio barrier. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=OpC-9aBBVJe.
[17] Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation, 2023. URL https://arxiv.org/abs/2302.00111.
[18] Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu. IM-PALA: Scalable distributed deep-RL with importance weighted actor-learner architectures. In Proceedings of the 35th International Conference on Machine Learning, 2018.
[19] William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, and Will Dabney. Revisiting fundamentals of experience replay. In Proceedings of the 37th International Conference on Machine Learning, 2020.
[20] E. C. Fieller, H. O. Hartley, and E. S. Pearson. Tests for rank correlation coefficients. i. Biometrika, 44(3/4):470-481, 1957. ISSN 00063444. URL http://www.jstor.org/stable/2332878.
[21] Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning, 2020.