[27] Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric P. Xing, Sergey Levine, and Ruslan Salakhutdinov. Efficient exploration via state marginal matching. CoRR, abs/1906.05274, 2019. URL http://arxiv.org/abs/1906.05274.
[28] Yoonho Lee, Huaxiu Yao, and Chelsea Finn. Diversify and disambiguate: Learning from underspecified data. CoRR, abs/2202.03418, 2022. URL https://arxiv.org/abs/2202.03418.
[29] Chenghao Li, Tonghan Wang, Chengjie Wu, Qianchuan Zhao, Jun Yang, and Chongjie Zhang. Celebrating diversity in shared multi-agent reinforcement learning. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 3991-4002, 2021. URL https://proceedings.neurips.cc/paper/2021/bit/20aee3a5f4643755a79ee5f6a73050ac-AAbstract.html.
[30] Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. Deep reinforcement learning for dialogue generation. In Jian Su, Xavier Carreras, and Kevin Duh, editors, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pages 1192-1202. The Association for Computational Linguistics, 2016. doi: 10.18653/v1/d16-1127. URL https://doi.org/10.18653/v1/d16-1127.
[31] Tianyi Lin, Chi Jin, and Michael I. Jordan. On gradient descent ascent for nonconvex-concave minimax problems. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 6083-6093. PMLR, 2020. URL http://proceedings.mlr.press/v119/lin20a.html.
[32] Hao Liu and Pieter Abbeel. Behavior from the void: Unsupervised active pre-training. Advances in Neural Information Processing Systems, 34:18459-18473, 2021.
[33] Iou-Jen Liu, Unnat Jain, Raymond A. Yeh, and Alexander G. Schwing. Cooperative exploration for multi-agent deep reinforcement learning. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 6826-6836. PMLR, 2021. URL http://proceedings.mlr.press/v139/liu21j.html.
[34] Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, and Thore Graepel. Emergent coordination through competition. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019. URL https://openreview.net/forum?id=BkG8sjR5Km.
[35] Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdelmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, and Nicolas Heess. From motor control to team play in simulated humanoid football. Sci. Robotics, 7(69), 2022. doi: 10.1126/scirobotics.ab0235. URL https://doi.org/10.1126/scirobotics.ab0235.
[36] Xiangyu Liu, Hangtian Jia, Ying Wen, Yujing Hu, Yingfeng Chen, Changjie Fan, Zhipeng Hu, and Yaodong Yang. Towards unifying behavioral and response diversity for open-ended learning in zero-sum games. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 941-952, 2021. URL https://proceedings.neurips.cc/paper/2021/hash/07bba581a2dd8d098a3be0f683560643-AAbstract.html.
[37] Qian Long, Zihan Zhou, Abhinav Gupta, Fei Fang, Yi Wu, and Xiaolong Wang. Evolutionary population curriculum for scaling multi-agent reinforcement learning. In International Conference on Learning Representations, 2020.