[2] Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuska. Integrating state representation learning into deep reinforcement learning. IEEE Robotics Autom. Lett., 3(2):1394-1401, 2018.
[3] Thomas D. Barrett, William R. Clements, Jakob N. Foerster, and A. I. Lvovsky. Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3243-3250, 2020.
[4] Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, and Koray Kavukcuoglu. Reinforcement learning with unsupervised auxiliary tasks. In 5th International Conference on Learning Representations (ICLR), 2017.
[5] Jelle Munk, Jens Kober, and Robert Babuska. Learning state representation for deep actor-critic control. In 55th IEEE Conference on Decision and Control (CDC), pages 4667-4673, 2016.
[6] Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine. Learning invariant representations for reinforcement learning without reconstruction. In 9th International Conference on Learning Representations (ICLR), 2021.
[7] Zhihai Wang, Jie Wang, Qi Zhou, Bin Li, and Houqiang Li. Sample-efficient reinforcement learning via conservative model-based actor-critic. In Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), pages 8612-8620, 2022.
[8] Zhihai Wang, Taoxing Pan, Qi Zhou, and Jie Wang. Efficient exploration in resource-restricted reinforcement learning. In Brian Williams, Yiling Chen, and Jennifer Neville, editors, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), pages 10279-10287, 2023.
[9] Rui Yang, Jie Wang, Zijie Geng, Mingxuan Ye, Shuiwang Ji, Bin Li, and Feng Wu. Learning task-relevant representations for generalization via characteristic functions of reward sequence distributions. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2242-2252, 2022.
[10] Jie Wang, Rui Yang, Zijie Geng, Zhihao Shi, Mingxuan Ye, Qi Zhou, Shuiwang Ji, Bin Li, Yongdong Zhang, and Feng Wu. Generalization in visual reinforcement learning with the reward sequence distribution. 2023. doi: 10.48550/arXiv.2302.09601.
[11] Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, and Philip Bachman. Data-efficient reinforcement learning with self-predictive representations. In 9th International Conference on Learning Representations, ICLR, 2021.
[12] Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. Learning latent dynamics for planning from pixels. In Proceedings of the 36th International Conference on Machine Learning(ICML), volume 97, pages 2555-2565, 2019.
[13] Zhaohan Daniel Guo, Bernardo Ávila Pires, Bilal Piot, Jean-Bastien Grill, Florent Alché, Rémi Munos, and Mohammad Gheshlaghi Azar. Bootstrap latent-predictive representations for multitask reinforcement learning. In Proceedings of the 37th International Conference on Machine Learning(ICML), volume 119, pages 3875-3886, 2020.
[14] Nikhil Mishra, Pieter Abbeel, and Igor Mordatch. Prediction and control with temporal segment models. In Proceedings of the 34th International Conference on Machine Learning(ICML), volume 70, pages 2459-2468, 2017.
[15] Sungwook Moon and Ahmed Helmy. Understanding periodicity and regularity of nodal encounters in mobile networks: A spectral analysis. In Proceedings of the Global Communications Conference. GLOBECOM, pages 1-5. IEEE, 2010.
[16] Di Wu, Siyuan Li, Jie Yang, and Mohamad Sawan. neuro2vec: Masked fourier spectrum prediction for neurophysiological representation learning. arXiv preprint arXiv:2204.12440, 2022.
[17] Anthony Tompkins and Fabio Ramos. Fourier feature approximations for periodic kernels in time-series modelling. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 4155–4162. AAAI Press, 2018.
[18] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
[19] Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama, and Daniel Nikovski. Can increasing input dimensionality improve deep reinforcement learning? In Proceedings of the 37th International Conference on Machine Learning, ICML, volume 119, pages 7424-7433, 2020.