[13] Christopher Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, and Honglak Lee. Successor feature landmarks for long-horizon goal-conditioned reinforcement learning. Advances in Neural Information Processing Systems, 34:26963-26975, 2021.
[14] Edward S Hu, Richard Chang, Oleh Rybkin, and Dinesh Jayaraman. Planning goals for exploration. arXiv preprint arXiv:2303.13002, 2023.
[15] Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, and Ding Zhao. Curriculum reinforcement learning using optimal transport via gradual domain adaptation. arXiv preprint arXiv:2210.10195, 2022.
[16] Zhiao Huang, Fangchen Liu, and Hao Su. Mapping state space using landmarks for universal goal reaching. Advances in Neural Information Processing Systems, 32, 2019.
[17] Riashat Islam, Hongyu Zang, Anirudh Goyal, Alex Lamb, Kenji Kawaguchi, Xin Li, Romain Laroche, Yoshua Bengio, and Remi Tachet Des Combes. Discrete factorial representations as an abstraction for goal conditioned reinforcement learning. arXiv preprint arXiv:2211.00247, 2022.
[18] Minqi Jiang, Edward Grefenstette, and Tim Rocktaschel. Prioritized level replay. In International Conference on Machine Learning, pages 4940-4950. PMLR, 2021.
[19] Yuu Jinnai, Joe Won Park, Marlos C Machado, and George Konidaris. Exploration in reinforcement learning with deep covering options. In International Conference on Learning Representations, 2020.
[20] Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, and Noam Shazeer. Fast decoding in sequence models using discrete latent variables. In International Conference on Machine Learning, pages 2390-2399. PMLR, 2018.
[21] Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, and Joni Pajarinen. Curriculum reinforcement learning via constrained optimal transport. In International Conference on Machine Learning, pages 11341-11358. PMLR, 2022.
[22] Martin Klissarov and Marlos C Machado. Deep laplacian-based options for temporally-extended exploration. arXiv preprint arXiv:2301.11181, 2023.
[23] 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.
[24] Seungjae Lee, Jigang Kim, Inkyu Jang, and H Jin Kim. Dhrl: A graph-based approach for long-horizon and sparse hierarchical reinforcement learning. Advances in Neural Information Processing Systems, 36, 2022.
[25] Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H Pong, Aurick Zhou, Justin Yu, and Sergey Levine. Mural: Meta-learning uncertainty-aware rewards for outcome-driven reinforcement learning. In International conference on machine learning, pages 6346-6356. PMLR, 2021.
[26] Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.
[27] Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, and Sai Rajeswar. Choreographer: Learning and adapting skills in imagination. arXiv preprint arXiv:2211.13350, 2022.
[28] Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak. Discovering and achieving goals via world models. Advances in Neural Information Processing Systems, 34:24379-24391, 2021.
[29] Sanmit Narvekar, Jivko Sinapov, and Peter Stone. Autonomous task sequencing for customized curriculum design in reinforcement learning. In IJCAI, pages 2536-2542, 2017.