High-Fidelity Data-Driven Dynamics Model for Reinforcement Learning-based Control in HL-3 Tokamak Article Swipe
Ni Wu
,
Zongyu Yang
,
Rongpeng Li
,
Ning Wei
,
Yihang Chen
,
Qianyun Dong
,
Jiyuan Li
,
Guohui Zheng
,
Xinwen Gong
,
Fei Gao
,
Bo Li
,
Min Xu
,
Zhifeng Zhao
,
W.L. Zhong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.09238
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.09238
The success of reinforcement learning (RL)-based control in tokamaks, an emerging technique for controlled nuclear fusion with improved flexibility, typically requires substantial interaction with a simulator capable of accurately evolving the high-dimensional plasma state. Compared to first-principle-based simulators, whose intense computations lead to sluggish RL training, we devise an effective method to acquire a fully data-driven simulator, by mitigating the arising compounding error issue due to the underlying autoregressive nature. With high accuracy and appealing extrapolation capability, this high-fidelity dynamics model subsequently enables the rapid training of a qualified RL agent to directly generate engineering-reasonable actuator commands, aiming at the desired long-term targets of plasma configuration. Together with a surrogate model for Equilibrium Fitting code based on neural network, named EFITNN, the RL agent successfully maintains a 400-ms, 1 kHz trajectory control with accurate waveform tracking of plasma current and last closed flux surface on the HL-3 tokamak. Furthermore, it also demonstrates the feasibility of zero-shot adaptation to changed triangularity targets, confirming the robustness of the developed data-driven dynamics model. Our work underscores the advantage of fully data-driven dynamics models in yielding RL-based trajectory control policies at a sufficiently fast pace, an anticipated engineering requirement in daily discharge practices for the upcoming ITER device.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.09238
- https://arxiv.org/pdf/2409.09238
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403662562
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403662562Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.09238Digital Object Identifier
- Title
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High-Fidelity Data-Driven Dynamics Model for Reinforcement Learning-based Control in HL-3 TokamakWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-14Full publication date if available
- Authors
-
Ni Wu, Zongyu Yang, Rongpeng Li, Ning Wei, Yihang Chen, Qianyun Dong, Jiyuan Li, Guohui Zheng, Xinwen Gong, Fei Gao, Bo Li, Min Xu, Zhifeng Zhao, W.L. ZhongList of authors in order
- Landing page
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https://arxiv.org/abs/2409.09238Publisher landing page
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https://arxiv.org/pdf/2409.09238Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2409.09238Direct OA link when available
- Concepts
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Tokamak, Reinforcement learning, Fidelity, Dynamics (music), Computer science, Control (management), High fidelity, Artificial intelligence, Physics, Plasma, Nuclear physics, Telecommunications, AcousticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.closed | 141 |
| abstract_inverted_index.devise | 47 |
| abstract_inverted_index.fusion | 15 |
| abstract_inverted_index.method | 50 |
| abstract_inverted_index.model. | 169 |
| abstract_inverted_index.models | 179 |
| abstract_inverted_index.neural | 117 |
| abstract_inverted_index.plasma | 32, 104, 137 |
| abstract_inverted_index.state. | 33 |
| abstract_inverted_index.400-ms, | 127 |
| abstract_inverted_index.EFITNN, | 120 |
| abstract_inverted_index.Fitting | 113 |
| abstract_inverted_index.acquire | 52 |
| abstract_inverted_index.arising | 60 |
| abstract_inverted_index.capable | 26 |
| abstract_inverted_index.changed | 158 |
| abstract_inverted_index.control | 6, 131, 184 |
| abstract_inverted_index.current | 138 |
| abstract_inverted_index.desired | 100 |
| abstract_inverted_index.device. | 203 |
| abstract_inverted_index.enables | 82 |
| abstract_inverted_index.intense | 39 |
| abstract_inverted_index.nature. | 69 |
| abstract_inverted_index.nuclear | 14 |
| abstract_inverted_index.success | 1 |
| abstract_inverted_index.surface | 143 |
| abstract_inverted_index.targets | 102 |
| abstract_inverted_index.Compared | 34 |
| abstract_inverted_index.RL-based | 182 |
| abstract_inverted_index.Together | 106 |
| abstract_inverted_index.accuracy | 72 |
| abstract_inverted_index.accurate | 133 |
| abstract_inverted_index.actuator | 95 |
| abstract_inverted_index.directly | 92 |
| abstract_inverted_index.dynamics | 79, 168, 178 |
| abstract_inverted_index.emerging | 10 |
| abstract_inverted_index.evolving | 29 |
| abstract_inverted_index.generate | 93 |
| abstract_inverted_index.improved | 17 |
| abstract_inverted_index.learning | 4 |
| abstract_inverted_index.network, | 118 |
| abstract_inverted_index.policies | 185 |
| abstract_inverted_index.requires | 20 |
| abstract_inverted_index.sluggish | 43 |
| abstract_inverted_index.targets, | 160 |
| abstract_inverted_index.tokamak. | 147 |
| abstract_inverted_index.tracking | 135 |
| abstract_inverted_index.training | 85 |
| abstract_inverted_index.upcoming | 201 |
| abstract_inverted_index.waveform | 134 |
| abstract_inverted_index.yielding | 181 |
| abstract_inverted_index.advantage | 174 |
| abstract_inverted_index.appealing | 74 |
| abstract_inverted_index.commands, | 96 |
| abstract_inverted_index.developed | 166 |
| abstract_inverted_index.discharge | 197 |
| abstract_inverted_index.effective | 49 |
| abstract_inverted_index.long-term | 101 |
| abstract_inverted_index.maintains | 125 |
| abstract_inverted_index.practices | 198 |
| abstract_inverted_index.qualified | 88 |
| abstract_inverted_index.simulator | 25 |
| abstract_inverted_index.surrogate | 109 |
| abstract_inverted_index.technique | 11 |
| abstract_inverted_index.tokamaks, | 8 |
| abstract_inverted_index.training, | 45 |
| abstract_inverted_index.typically | 19 |
| abstract_inverted_index.zero-shot | 155 |
| abstract_inverted_index.(RL)-based | 5 |
| abstract_inverted_index.accurately | 28 |
| abstract_inverted_index.adaptation | 156 |
| abstract_inverted_index.confirming | 161 |
| abstract_inverted_index.controlled | 13 |
| abstract_inverted_index.mitigating | 58 |
| abstract_inverted_index.robustness | 163 |
| abstract_inverted_index.simulator, | 56 |
| abstract_inverted_index.trajectory | 130, 183 |
| abstract_inverted_index.underlying | 67 |
| abstract_inverted_index.Equilibrium | 112 |
| abstract_inverted_index.anticipated | 192 |
| abstract_inverted_index.capability, | 76 |
| abstract_inverted_index.compounding | 61 |
| abstract_inverted_index.data-driven | 55, 167, 177 |
| abstract_inverted_index.engineering | 193 |
| abstract_inverted_index.feasibility | 153 |
| abstract_inverted_index.interaction | 22 |
| abstract_inverted_index.requirement | 194 |
| abstract_inverted_index.simulators, | 37 |
| abstract_inverted_index.substantial | 21 |
| abstract_inverted_index.underscores | 172 |
| abstract_inverted_index.Furthermore, | 148 |
| abstract_inverted_index.computations | 40 |
| abstract_inverted_index.demonstrates | 151 |
| abstract_inverted_index.flexibility, | 18 |
| abstract_inverted_index.subsequently | 81 |
| abstract_inverted_index.successfully | 124 |
| abstract_inverted_index.sufficiently | 188 |
| abstract_inverted_index.extrapolation | 75 |
| abstract_inverted_index.high-fidelity | 78 |
| abstract_inverted_index.reinforcement | 3 |
| abstract_inverted_index.triangularity | 159 |
| abstract_inverted_index.autoregressive | 68 |
| abstract_inverted_index.configuration. | 105 |
| abstract_inverted_index.high-dimensional | 31 |
| abstract_inverted_index.first-principle-based | 36 |
| abstract_inverted_index.engineering-reasonable | 94 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile |