참고문헌

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John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, and Pieter Abbeel. High-Dimensional Continuous Control Using Generalized Advantage Estimation. In Yoshua Bengio and Yann LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. 2016. URL: http://arxiv.org/abs/1506.02438.

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John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust Region Policy Optimization. In Francis Bach and David Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, 1889–1897. Lille, France, 2015. PMLR. URL: https://proceedings.mlr.press/v37/schulman15.html.

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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal Policy Optimization Algorithms. 2017. arXiv:1707.06347.

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Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. Curiosity-driven exploration by self-supervised prediction. 2017. arXiv:1705.05363.

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Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov. Exploration by random network distillation. 2018. arXiv:1810.12894.

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Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta. Self-supervised exploration via disagreement. 2019. arXiv:1906.04161.

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Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto. Reinforcement Learning with Prototypical Representations. 2021. arXiv:2102.11271.

9

Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. Diversity is all you need: learning skills without a reward function. 2018. arXiv:1802.06070.

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Steven Hansen, Will Dabney, Andre Barreto, Tom Van de Wiele, David Warde-Farley, and Volodymyr Mnih. Fast task inference with variational intrinsic successor features. 2020. arXiv:1906.05030.

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Hao Liu and Pieter Abbeel. Aps: active pretraining with successor features. 2021. arXiv:2108.13956.

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Hyunseung Kim, Byungkun Lee, Hojoon Lee, Dongyoon Hwang, Sejik Park, Kyushik Min, and Jaegul Choo. Learning to discover skills through guidance. 2023. arXiv:2310.20178.

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Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, and Gunhee Kim. Lipschitz-constrained unsupervised skill discovery. 2022. arXiv:2202.00914.

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Seohong Park, Oleh Rybkin, and Sergey Levine. Metra: scalable unsupervised rl with metric-aware abstraction. 2024. arXiv:2310.08887.

15

Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin, Pieter Abbeel, Alessandro Lazaric, and Lerrel Pinto. Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning. 2022. arXiv:2201.13425.

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Camilo Gordillo, Joakim Bergdahl, Konrad Tollmar, and Linus Gisslén. Improving playtesting coverage via curiosity driven reinforcement learning agents. 2021. arXiv:2103.13798.