Seo, Younggyo, Hafner, Danijar, Liu, Hao, Liu, Fangchen, James, Stephen, Lee, Kimin, and Abbeel, Pieter. Masked world models for visual control. In Conference on Robot Learning, 2022a.
Seo, Younggyo, Lee, Kimin, James, Stephen, and Abbeel, Pieter. Reinforcement learning with action-free pre-training from videos. In International Conference on Machine Learning, 2022b.
Seyde, Tim, Schwarting, Wilko, Karaman, Sertac, and Rus, Daniela. Learning to plan optimistically: Uncertainty-guided deep exploration via latent model ensembles. In Conference on Robot Learning, 2021.
Singh, Harshinder, Misra, Neeraj, Hnizdo, Vladimir, Fedorowicz, Adam, and Demchuk, Eugene. Nearest neighbor estimates of entropy. American journal of mathematical and management sciences, 2003.
Stadie, Bradley C., Levine, Sergey, and Abbeel, Pieter. Incentivizing exploration in reinforcement learning with deep predictive models. arXiv preprint arXiv:1507.00814, 2015.
Sutton, Richard S and Barto, Andrew G. Reinforcement learning: An introduction. MIT Press, 2018.
Tang, Haoran, Houthooft, Rein, Foote, Davis, Stooke, Adam, Chen, OpenAI Xi, Duan, Yan, Schulman, John, DeTurck, Filip, and Abbeel, Pieter. # exploration: A study of count-based exploration for deep reinforcement learning. In Advances in Neural Information Processing Systems, 2017.
Tao, Ruo Yu, François-Lavet, Vincent, and Pineau, Joelle. Novelty search in representational space for sample efficient exploration. In Advances in Neural Information Processing Systems, 2020.
Tassa, Yuval, Tunyasuvunakool, Saran, Muldal, Alistair, Doron, Yotam, Liu, Siqi, Bohez, Steven, Merel, Josh, Erez, Tom, Lillicrap, Timothy, and Heess, Nicolas. dm_control: Software and tasks for continuous control. arXiv preprint arXiv:2006.12983, 2020.
Thrun, Sebastian. Efficient exploration in reinforcement learning. Technical Report. Carnegie Mellon University, 1992a.
Thrun, Sebastian B. The role of exploration in learning control with neural networks. Handbook of intelligent control: Neural, fuzzy and adaptive approaches, 1992b.
Tokic, Michel. Adaptive  $\varepsilon$ -greedy exploration in reinforcement learning based on value differences. In KI 2010: Advances in Artificial Intelligence: 33rd Annual German Conference on AI, Karlsruhe, Germany, September 21-24, 2010. Proceedings 33. Springer, 2010.
Wawrzynski, Pawel. Control policy with autocorrelated noise in reinforcement learning for robotics. International Journal of Machine Learning and Computing, 2015.
Whitney, William F, Bloesch, Michael, Springenberg, Jost Tobias, Abdolmaleki, Abbas, Cho, Kyunghyun, and Riedmiller, Martin. Decoupled exploration and exploitation policies for sample-efficient reinforcement learning. arXiv preprint arXiv:2101.09458, 2021.
Yang, Qisong and Spanan, Matthijs TJ. Cem: Constrained entropy maximization for task-agnostic safe exploration. In The Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023.
Yarats, Denis, Fergus, Rob, Lazaric, Alessandro, and Pinto, Lerrel. Mastering visual continuous control: Improved data-augmented reinforcement learning. arXiv preprint arXiv:2107.09645, 2021a.
Yarats, Denis, Fergus, Rob, Lazaric, Alessandro, and Pinto, Lerrel. Reinforcement learning with prototypical representations. In International Conference on Machine Learning, 2021b.
Yu, Tianhe, Quillen, Deirdre, He, Zhanpeng, Julian, Ryan, Hausman, Karol, Finn, Chelsea, and Levine, Sergey. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning. In Conference on Robot Learning, 2020.
Yuan, Mingqi, Pun, Man-On, and Wang, Dong. Rényi state entropy maximization for exploration acceleration in reinforcement learning. IEEE Transactions on Artificial Intelligence, 2022.