[36] Karl Kunisch and Thomas Pock. A bilevel optimization approach for parameter learning in variational models. SIAM Journal on Imaging Sciences, 2013.
[37] Jan Larsen, Lars Kai Hansen, Claus Svarer, and M Ohlsson. Design and regularization of neural networks: the optimal use of a validation set. In Neural Networks for Signal Processing VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop, 1996.
[38] Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto. Meta-learning with differentiable convex optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
[39] Michael L Littman, Ufuk Topcu, Jie Fu, Charles Isbell, Min Wen, and James MacGlashan. Environment-independent task specifications via GLTL. arXiv preprint arXiv:1704.04341, 2017.
[40] Jonathan Lorraine, Paul Vicol, and David Duvenaud. Optimizing millions of hyperparameters by implicit differentiation. In International Conference on Artificial Intelligence and Statistics, 2020.
[41] Andrew Y Ng, Daishi Harada, and Stuart Russell. Policy invariance under reward transformations: Theory and application to reward shaping. In ICML, 1999.
[42] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. CoRR, 2018.
[43] Scott Niekum, Andrew G Barto, and Lee Spector. Genetic programming for reward function search. IEEE Transactions on Autonomous Mental Development, 2010.
[44] Chris Nota and Philip S. Thomas. Is the policy gradient a gradient? arXiv preprint arXiv:1906.07073, 2020.
[45] Junhyuk Oh, Matteo Hessel, Wojciech M Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, and David Silver. Discovering reinforcement learning algorithms. arXiv preprint arXiv:2007.08794, 2020.
[46] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 2019.
[47] Barak A Pearlmutter. Fast exact multiplication by the hessian. Neural computation, 1994.
[48] Silviu Pitis. Rethinking the discount factor in reinforcement learning: A decision theoretic approach. In Proceedings of the AAAI Conference on Artificial Intelligence, 2019.
[49] Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine. Meta-learning with implicit gradients. In Advances in Neural Information Processing Systems, 2019.
[50] Aravind Rajeswaran, Igor Mordatch, and Vikash Kumar. A game theoretic framework for model based reinforcement learning. arXiv preprint arXiv:2004.07804, 2020.
[51] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
[52] Satinder Singh, Richard L Lewis, and Andrew G Barto. Where do rewards come from. In Proceedings of the Annual Conference of the Cognitive Science Society, 2009.
[53] Satinder Singh, Richard L Lewis, Andrew G Barto, and Jonathan Sorg. Intrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Transactions on Autonomous Mental Development, 2010.
[54] Jonathan Sorg, Richard L Lewis, and Satinder Singh. Reward design via online gradient ascent. Advances in Neural Information Processing Systems, 2010.