Scott Fahlman and Christian Lebiere. The cascade-correlation learning architecture. Advances in neural information processing systems, 2, 1989.
Amir Farahmand, Mohammad Ghavamzadeh, Shie Mannor, and Csaba Szepesvári. Regularized policy iteration. Advances in Neural Information Processing Systems, 21, 2008.
Robert M French. Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 3 (4):128-135, 1999.
Florin Gogianu, Tudor Berariu, Mihaela C Rosca, Claudia Clopath, Lucian Busoniu, and Razvan Pascanu. Spectral normalisation for deep reinforcement learning: an optimisation perspective. In International Conference on Machine Learning, pages 3734-3744. PMLR, 2021.
Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matt Hoffman, Razvan Pascanu, and Arnaud Doucet. An empirical study of implicit regularization in deep offline rl. arXiv preprint arXiv:2207.02099, 2022.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. Rainbow: Combining improvements in deep reinforcement learning. In Thirty-second AAAI conference on artificial intelligence, 2018.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022.
John D Hunter. Matplotlib: A 2d graphics environment. IEEE Annals of the History of Computing, 9 (03):90-95, 2007.
Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, and Shimon Whiteson. Transient non-stationarity and generalisation in deep reinforcement learning. In International Conference on Learning Representations, 2021.
Eric Jones, Travis Oliphant, and PEARU Peterson. SciPy: Open source scientific tools for Python. 2014.
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, et al. Model-based reinforcement learning for atari. arXiv preprint arXiv:1903.00374, 2019.
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian E Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica B Hamrick, Jason Grout, Sylvain Corlay, et al. Jupyter Notebooks-a publishing format for reproducible computational workflows., volume 2016. 2016.
Bryan Kolb and Robbin Gibb. Brain plasticity and behaviour in the developing brain. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 20(4):265, 2011.
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, and Sergey Levine. Implicit under-parameterization inhibits data-efficient deep reinforcement learning. In International Conference on Learning Representations, 2021.
Qiyang Li, Aviral Kumar, Ilya Kostrikov, and Sergey Levine. Efficient deep reinforcement learning requires regulating overfitting. In The Eleventh International Conference on Learning Representations, 2023.
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.
Long-Ji Lin. Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine learning, 8(3):293-321, 1992.