https://scholars.lib.ntu.edu.tw/handle/123456789/415605
標題: | Generation of ice states through deep reinforcement learning | 作者: | Zhao, Kai Wen Kao, Wen Han Wu, Kai Hsin YING-JER KAO |
關鍵字: | Physics - Disordered Systems and Neural Networks; Physics - Disordered Systems and Neural Networks; Physics - Statistical Mechanics | 公開日期: | 5-六月-2019 | 卷: | 99 | 期: | 6 | 來源出版物: | Physical Review E | 摘要: | © 2019 American Physical Society. We present a deep reinforcement learning framework where a machine agent is trained to search for a policy to generate a ground state for the square ice model by exploring the physical environment. After training, the agent is capable of proposing a sequence of local moves to achieve the goal. Analysis of the trained policy and the state value function indicates that the ice rule and loop-closing condition are learned without prior knowledge. We test the trained policy as a sampler in the Markov chain Monte Carlo and benchmark against the baseline loop algorithm. This framework can be generalized to other models with topological constraints where generation of constraint-preserving states is difficult. |
描述: | 10 pages, 13 figures, 2 tables. Published version |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/415605 | ISSN: | 24700045 | DOI: | 10.1103/PhysRevE.99.062106 |
顯示於: | 物理學系 |
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