[54] Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbuhl, Vladlen Koltun, and Dawn Song. Assessing generalization in deep reinforcement learning. arXiv preprint arXiv:1810.12282, 2018.
[55] Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, et al. Stabilizing transformers for reinforcement learning. In International conference on machine learning, pages 7487-7498. PMLR, 2020.
[56] PA Politzer, CC Petty, RJ Jayakumar, TC Luce, MR Wade, JC DeBoo, JR Ferron, P Gohil, CT Holcomb, AW Hyatt, et al. Influence of toroidal rotation on transport and stability in hybrid scenario plasmas in dii-d. Nuclear Fusion, 48(7):075001, 2008.
[57] Vitchy R. Pong and Ashvin Nair. rkit. https://github.com/rail-berkeley/r1kit, 2018-.
[58] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
[59] Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, and Sergey Levine. Epopt: Learning robust neural network policies using model ensembles. arXiv preprint arXiv:1610.01283, 2016.
[60] Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen. Efficient off-policy meta-reinforcement learning via probabilistic context variables. In International conference on machine learning, pages 5331-5340. PMLR, 2019.
[61] H Reimerdes, AM Garofalo, GL Jackson, M Okabayashi, EJ Strait, Ming-Sheng Chu, Y In, RJ La Haye, MJ Lanctot, YQ Liu, et al. Reduced critical rotation for resistive-wall mode stabilization in a near-axisymmetric configuration. Physical review letters, 98(5):055001, 2007.
[62] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
[63] JT Scoville, DA Humphreys, JR Ferron, and P Gohil. Simultaneous feedback control of plasma rotation and stored energy on the dii-d tokamak. Fusion engineering and design, 82(5-14): 1045-1050, 2007.
[64] J Seo, Y-S Na, B Kim, CY Lee, MS Park, SJ Park, and YH Lee. Development of an operation trajectory design algorithm for control of multiple 0d parameters using deep reinforcement learning in kstar. *Nuclear Fusion*, 62(8):086049, 2022.
[65] Jaemin Seo, Y-S Na, B Kim, CY Lee, MS Park, SJ Park, and YH Lee. Feedforward beta control in the kstar tokamak by deep reinforcement learning. *Nuclear Fusion*, 61(10):106010, 2021.
[66] Open Ended Learning Team, Adam Stooke, Anuj Mahajan, Catarina Barros, Charlie Deck, Jakob Bauer, Jakub Sygnowski, Maja Trebacz, Max Jaderberg, Michael Mathieu, et al. Open-ended learning leads to generally capable agents. arXiv preprint arXiv:2107.12808, 2021.
[67] B Tobias, M Chen, IGJ Classen, CW Domier, R Fitzpatrick, BA Grierson, NC Luhmann Jr, CM Muscatello, M Okabayashi, KEJ Olofsson, et al. Rotation profile flattening and toroidal flow shear reversal due to the coupling of magnetic islands in tokamaks. Physics of Plasmas, 23 (5):056107, 2016.
[68] ITER Physics Expert Group on Confinement Transport, , ITER Physics Expert Group on Confinement Modelling Database, , and ITER Physics Basis Editors. Chapter 2: Plasma confinement and transport. Nuclear Fusion, 39(12):2175-2249, 1999.
[69] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[70] Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michael Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al. Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782):350-354, 2019.