[16] Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, et al. Threadworld: A platform for interactive multi-modal physical simulation. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021.
[17] Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiaing Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, and Hao Su. Maniskill2: A unified benchmark for generalizable manipulation skills. In International Conference on Learning Representations, 2023.
[18] William H Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, and Ruslan Salakhutdinov. Minerl: a large-scale dataset of minecraft demonstrations. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 2442-2448, 2019.
[19] Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning, pages 1861-1870. PMLR, 2018.
[20] Danijar Hafner. Benchmarking the spectrum of agent capabilities. In International Conference on Learning Representations, 2021.
[21] 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.
[22] Nicklas Hansen and Xiaolong Wang. Generalization in reinforcement learning by soft data augmentation. In International Conference on Robotics and Automation, 2021.
[23] 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, 2021.
[24] Yangru Huang Haoran Xu, Ding Chen. Carlaenv-benchmark, 2022.
[25] Yangru Huang, Peixi Peng, Yifan Zhao, Guangyao Chen, and Yonghong Tian. Spectrum random masking for generalization in image-based reinforcement learning. Advances in Neural Information Processing Systems, 35:20393-20406, 2022.
[26] Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell. The malmo platform for artificial intelligence experimentation.
[27] Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius, and Danny Lange. Obstacle tower: A generalization challenge in vision, control, and planning. arXiv preprint arXiv:1902.01378, 2019.
[28] Kibeom Kim, Min Whoo Lee, Yoonsung Kim, JeHwan Ryu, Minsu Lee, and Byoung-Tak Zhang. Goal-aware cross-entropy for multi-target reinforcement learning. Advances in Neural Information Processing Systems, 34:2783–2795, 2021.
[29] Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. A survey of generalisation in deep reinforcement learning. arXiv preprint arXiv:2111.09794, 2021.
[30] Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. A survey of zero-shot generalisation in deep reinforcement learning. Journal of Artificial Intelligence Research, 76: 201-264, 2023.
[31] Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. Wilds: A benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning, pages 5637-5664. PMLR, 2021.
[32] 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.