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      "Timo Ewalds",
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      "Diego de las Casas",
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        "title": "Magnetic control of tokamak plasmas through deep reinforcement learning",
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        "title": "Magnetic control of tokamak plasmas through deep reinforcement learning — deposited bibliography",
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      "statement": "# Learned magnetic control of tokamak plasmas\n\nA deep reinforcement-learning controller produced and maintained multiple plasma configurations in physical experiments on the TCV tokamak.\n\n## Scientific scope\n\nHuman teams supplied the facility, simulation, objectives and experimental validation. Plasma-shape control does not establish fusion energy breakeven or a working power plant.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nJonas Degrave; Federico Felici; Jonas Buchli; Michael Neunert; Brendan Tracey; Francesco Carpanese; Timo Ewalds; Roland Hafner; Abbas Abdolmaleki; Diego de las Casas; Craig Donner; Leslie Fritz; Cristian Galperti; Andrea Huber; James Keeling; Maria Tsimpoukelli; Jackie Kay; Antoine Merle; Jean-Marc Moret; Seb Noury; Federico Pesamosca; David Pfau; Olivier Sauter; Cristian Sommariva; Stefano Coda; Basil Duval; Ambrogio Fasoli; Pushmeet Kohli; Koray Kavukcuoglu; Demis Hassabis; Martin Riedmiller\n\nSource paper byline retained, including repeated label positions. Names are not verified unique identities or measurements of individual effort.\n\nTCV learned magnetic controller / DeepMind and EPFL/SPC research collaboration / Deep reinforcement-learning control policy; no public model release identifier: Learned control policies and executed magnetic control within the human-designed experimental system (source reported).\n\n## Original sources\n\n- [Magnetic control of tokamak plasmas through deep reinforcement learning](https://doi.org/10.1038/s41586-021-04301-9)\n- [Magnetic control of tokamak plasmas through deep reinforcement learning — deposited bibliography](https://api.crossref.org/works/10.1038%2Fs41586-021-04301-9)\n- [Full original plasma-control paper](https://pmc.ncbi.nlm.nih.gov/articles/PMC8850200/)\n",
      "summary": "A deep reinforcement-learning controller produced and maintained multiple plasma configurations in physical experiments on the TCV tokamak.",
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