[2] Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Yoshua Bengio, Bernhard Schölkopf, Manuel Wüthrich, and Stefan Bauer. Causalworld: A robotic manipulation benchmark for causal structure and transfer learning. arXiv preprint arXiv:2010.04296, 2020.
[3] Dafni Antotsiou, Carlo Ciliberto, and Tae-Kyun Kim. Adversarial imitation learning with trajectoryal augmentation and correction. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 4724-4730. IEEE, 2021.
[4] Léon Bottou, Jonas Peters, Joaquin Quinonero-Candela, Denis X Charles, D Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson. Counterfactual reasoning and learning systems: The example of computational advertising. Journal of Machine Learning Research, 14(11), 2013.
[5] Ivan Bratko, Tanja Urbancic, and Claude Sammut. Behavioural cloning: phenomena, results and problems. IFAC Proceedings Volumes, 28(21):143-149, 1995.
[6] Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, and Nicolas Heess. Woulda, coulda, shoulda: Counterfactually-guided policy search. arXiv preprint arXiv:1811.06272, 2018.
[7] Satoshi Hoshino, Tomoki Hisada, and Ryota Oikawa. Imitation learning based on data augmentation for robotic reaching. In 2021 60th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pages 417-424. IEEE, 2021.
[8] Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C Lipton. Explaining the efficacy of counterfactually augmented data. arXiv preprint arXiv:2010.02114, 2020.
[9] Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen. Variational autoencoders and nonlinear ica: A unifying framework. In International Conference on Artificial Intelligence and Statistics, pages 2207-2217. PMLR, 2020.
[10] Taylor W Killian, Marzyeh Ghassemi, and Shalmali Joshi. Counterfactually guided policy transfer in clinical settings. In Conference on Health, Inference, and Learning, pages 5-31. PMLR, 2022.
[11] Beomjoon Kim and Joelle Pineau. Socially adaptive path planning in human environments using inverse reinforcement learning. International Journal of Social Robotics, 8:51-66, 2016.
[12] Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang, and Kee-Eung Kim. Lobs-dice: offline imitation learning from observation via stationary distribution correction estimation. arXiv preprint arXiv:2202.13536, 2022.
[13] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
[14] Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine. Stabilizing off-policy q-learning via bootstrapping error reduction. Advances in Neural Information Processing Systems, 32, 2019.
[15] Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, and Ping Luo. Chipformer: Transferable chip placement via offline decision transformer. arXiv preprint arXiv:2306.14744, 2023.
[16] Jinxin Liu, Li He, Yachen Kang, Zifeng Zhuang, Donglin Wang, and Huazhe Xu. Ceil: Generalized contextual imitation learning. arXiv preprint arXiv:2306.14534, 2023.
[17] Jinxin Liu, Lipeng Zu, Li He, and Donglin Wang. Clue: Calibrated latent guidance for offline reinforcement learning. arXiv preprint arXiv:2306.13412, 2023.
[18] Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. Challenging common assumptions in the unsupervised learning of disentangled representations. In international conference on machine learning, pages 4114-4124. PMLR, 2019.