[36] Brandon Trabucco, Aviral Kumar, Xinyang Geng, and Sergey Levine. Conservative objective models for effective offline model-based optimization. In International Conference on Machine Learning, pages 10358-10368. PMLR, 2021.
[37] Sen Zhao, Erez Louidor, and Maya Gupta. Global optimization networks. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 26927-26957. PMLR, 17-23 Jul 2022.
[38] Fengji Yi, Wenlong Fu, and Huan Liang. Model-based reinforcement learning: A survey. 2018.
[39] Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine. Soft Actor-Critic Algorithms and Applications. arXiv, 2018.
[40] S Phaniteja, Parijit Dewangan, Pooja Guhan, Abhishek Sarkar, and K Madhava Krishna. A deep reinforcement learning approach for dynamically stable inverse kinematics of humanoid robots. In 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1818-1823. IEEE, 2017.
[41] Bhuvanesh Sridharan, Manan Goel, and U Deva Priyakumar. Modern machine learning for tackling inverse problems in chemistry: molecular design to realization. Chemical Communications, 58(35):5316-5331, 2022.
[42] Ruiyuan Kang, Dimitrios C. Kyritsis, and Panos Liatsis. Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling, October 2022.
[43] Daphna Joel, Yael Niv, and Eytan Ruppin. Actor-critic models of the basal ganglia: New anatomical and computational perspectives. *Neural networks*, 15(4-6):535–547, 2002.
[44] Jürgen Schmidhuber. On learning to think: Algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models. arXiv preprint arXiv:1511.09249, 2015.
[45] Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Daniele Giardino, Marco Re, and Sergio Spanò. Multi-agent reinforcement learning: A review of challenges and applications. Applied Sciences, 11(11):4948, 2021.
[46] Yongshuai Liu, Avishai Halev, and Xin Liu. Policy learning with constraints in model-free reinforcement learning: A survey. In The 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021.
[47] Kazuo Yonekura and Hitoshi Hattori. Framework for design optimization using deep reinforcement learning. Structural and Multidisciplinary Optimization, 60:1709-1713, 2019.
[48] Xinyu Hui, Hui Wang, Wenqiang Li, Junqiang Bai, Fei Qin, and Guoqiang He. Multi-object aerodynamic design optimization using deep reinforcement learning. AIP Advances, 11(8):085311, 2021.
[49] Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley. Optimization of molecules via deep reinforcement learning. Scientific reports, 9(1):1-10, 2019.
[50] Jonathan Viverat, Jean Rabault, Alexander Kuhnle, Hassan Ghraieb, Aurélien Larcher, and Elie Hachem. Direct shape optimization through deep reinforcement learning. Journal of Computational Physics, 428:110080, 2021.
[51] Ling Liu, Wenqi Wei, Ka-Ho Chow, Margaret Loper, Emre Gursoy, Stacey Truex, and Yanzhao Wu. Deep neural network ensembles against deception: Ensemble diversity, accuracy and robustness. In 2019 IEEE 16th international conference on mobile ad hoc and sensor systems (MASS), pages 274-282. IEEE, 2019.
[52] Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, et al. A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion, 76:243-297, 2021.