[12] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning (ICML), 2020.
[13] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. Decoupling representation learning from reinforcement learning. In International Conference on Machine Learning (ICML), 2020.
[14] Peng Cui and Susan Athey. Stable learning establishes some common ground between causal inference and machine learning. Nature Machine Intelligence, 4(2):110-115, 2022.
[15] Mhairi Dunion, Trevor McInroe, Kevin Luck, Josiah Hanna, and Stefano V Albrecht. Temporal disentanglement of representations for improved generalisation in reinforcement learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2023.
[16] Arthur Gretton, Kenji Fukumizu, Choon Teo, Le Song, Bernhard Scholkopf, and Alex Smola. A kernel statistical test of independence. Advances in neural information processing systems, 20, 2007.
[17] T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International Conference on Machine Learning (PMLR), page 1861-1870, 2018.
[18] Aleksi Hamalainen, Karol Arndt, Ali Ghadirzadeh, and Ville Kyrki. Affordance learning for end-to0end visuomotor robot control. In International Conference on Intelligent Robots and Systems (IROS), 2019.
[19] Nicklas Hansen and Xiaolong Wang. Generalization in reinforcement learning by soft data augmentation. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 13611-13617. IEEE, 2021.
[20] Nicklas Hansen, Hao Su, and Xiaolong Wang. Stabilizing deep q-learning with convnets and vision transformers under data augmentation. Advances in neural information processing systems, 34:3680-3693, 2021.
[21] Nicklas Hansen, Hao Su, and Xiaolong Wang. Stabilizing deep q-learning with convnets and vision transformers under data augmentation. Advances in neural information processing systems, 34:3680-3693, 2021.
[22] Jifeng Hu, Yanchao Sun, Hechang Chen, Sili Huang, Yi Chang, Lichao Sun, et al. Distributional reward estimation for effective multi-agent deep reinforcement learning. Advances in Neural Information Processing Systems, 35:12619-12632, 2022.
[23] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017.
[24] Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. A survey of generalisation in deep reinforcement learning. arXiv preprint arXiv:2111.09794, 2021.
[25] Ilya Kostrikov, Denis Yarats, and Rob Fergus. Image augmentation is all you need: Regularizing deep reinforcement learning from pixels. arXiv preprint arXiv:2004.13649, 2020.
[26] Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Xiong, and Bo Li. Stable prediction across unknown environments. In proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 1617-1626, 2018.
[27] Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, and Bo Li. Stable prediction with model misspecification and agnostic distribution shift. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2020.
[28] Michael Laskin, Aravind Srinivas, and Pieter Abbeel. Curl: Contrastive unsupervised representations for reinforcement learning. In International Conference on Machine Learning, pages 5639-5650. PMLR, 2020.