[1] Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel. Constrained policy optimization. In International conference on machine learning, pages 22-31. PMLR, 2017.
[2] Sanae Amani, Christos Thrampoulidis, and Lin Yang. Safe reinforcement learning with linear function approximation. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 243-253. PMLR, 18-24 Jul 2021.
[3] Sanae Amani, Christos Thrampoulidis, and Lin Yang. Safe reinforcement learning with linear function approximation. In International Conference on Machine Learning, pages 243-253. PMLR, 2021.
[4] Brandon Amos and J Zico Kolter. Optnet: Differentiable optimization as a layer in neural networks. In International Conference on Machine Learning, pages 136-145. PMLR, 2017.
[5] Kavosh Asadi, Dipendra Misra, and Michael Littman. Lipschitz continuity in model-based reinforcement learning. In International Conference on Machine Learning, pages 264-273. PMLR, 2018.
[6] Alex Ayoub, Zeyu Jia, Csaba Szepesvari, Mengdi Wang, and Lin Yang. Model-based reinforcement learning with value-targeted regression. In International Conference on Machine Learning, pages 463-474. PMLR, 2020.
[7] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 1877-1901. Curran Associates, Inc., 2020.
[8] Lukas Brunke, Melissa Greeff, Adam W Hall, Zhaocong Yuan, Siqi Zhou, Jacopo Panerati, and Angela P Schoellig. Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5:411-444, 2022.
[9] Duncan S Callaway, Meredith Fowlie, and Gavin McCormick. Location, location, location: The variable value of renewable energy and demand-side efficiency resources. Journal of the Association of Environmental and Resource Economists, 5(1):39-75, 2018.
[10] Agustin Castellano, Hancheng Min, Juan Bazerque, and Enrique Mallada. Reinforcement learning with almost sure constraints. In Learning for Dynamics and Control, 2022.
[11] Bingqing Chen, Priya L Donti, Kyri Baker, J Zico Kolter, and Mario Bergés. Enforcing policy feasibility constraints through differentiable projection for energy optimization. In Proceedings of the Twelfth ACM International Conference on Future Energy Systems, pages 199–210, 2021.
[12] Shuang Chen, Christina Delimitrou, and José F Martínez. Parties: Qos-aware resource partitioning for multiple interactive services. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, pages 107-120, 2019.