[13] Weiqin Chen, Dharmashankar Subramanian, and Santiago Paternain. Policy gradients for probabilistic constrained reinforcement learning, 2022.
[14] Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, et al. An adaptive deep rl method for non-stationary environments with piecewise stable context. Advances in Neural Information Processing Systems, 35:35449-35461, 2022.
[15] Xin Chen, Guannan Qu, Yujie Tang, Steven Low, and Na Li. Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision. arXiv, 2021.
[16] Yuri Chervonyi, Praneet Dutta, Piotr Trochim, Octavian Voicu, Cosmin Paduraru, Crystal Qian, Emre Karagozler, Jared Quincy Davis, Richard Chippendale, Gautam Bajaj, et al. Semi-analytical industrial cooling system model for reinforcement learning. arXiv preprint arXiv:2207.13131, 2022.
[17] Nicolas Christianson, Junxuan Shen, and Adam Wierman. Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems. In AI STATs, 2023.
[18] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms. In Proceedings of the 26th Symposium on Operating Systems Principles, pages 153-167, 2017.
[19] Google DeepMind. Safety-first ai for autonomous data centre cooling and industrial control. https://www_deepmind.com/blog/safety-first-ai-for-autonomous-data-centre-cooling-and-industrial-contro2018.
[20] Christina Delimitrou and Christos Kozyrakis. Paragon: Qos-aware scheduling for heterogeneous datacenters. ACM SIGPLAN Notices, 48(4):77-88, 2013.
[21] Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, and Mihailo Jovanovic. Provably efficient safe exploration via primal-dual policy optimization. In International Conference on Artificial Intelligence and Statistics, pages 3304-3312. PMLR, 2021.
[22] Dongsheng Ding, Kaiqing Zhang, Tamer Basar, and Mihailo Jovanovic. Natural policy gradient primal-dual method for constrained markov decision processes. Advances in Neural Information Processing Systems, 33:8378-8390, 2020.
[23] Jesse Dodge, Taylor Prewitt, Remi Tachet des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah A Smith, Nicole DeCario, and Will Buchanan. Measuring the carbon intensity of ai in cloud instances. In 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 1877-1894, 2022.
[24] Priya L Donti, David Rolnick, and J Zico Kolter. Dc3: A learning method for optimization with hard constraints. arXiv preprint arXiv:2104.12225, 2021.
[25] Yihan Du, Siwei Wang, and Longbo Huang. A one-size-fits-all solution to conservative bandit problems. In AAAI, 2021.
[26] Yonathan Efroni, Shie Mannor, and Matteo Pirotta. Exploration-exploitation in constrained mdps. arXiv preprint arXiv:2003.02189, 2020.
[27] Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, and Matteo Pirotta. Conservative exploration in reinforcement learning. In International Conference on Artificial Intelligence and Statistics, pages 1431-1441. PMLR, 2020.
[28] Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, and Marc G Bellemare. Deep-Mdp: Learning continuous latent space models for representation learning. In International Conference on Machine Learning, pages 2170-2179. PMLR, 2019.
[29] Arnob Ghosh, Xingyu Zhou, and Ness Shroff. Provably efficient model-free constrained rl with linear function approximation. arXiv preprint arXiv:2206.11889, 2022.