|Generation of ice states through deep reinforcement learning
|Zhao, Kai Wen
Kao, Wen Han
Wu, Kai Hsin
|Physics - Disordered Systems and Neural Networks; Physics - Disordered Systems and Neural Networks; Physics - Statistical Mechanics
|Physical Review E
© 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.
10 pages, 13 figures, 2 tables. Published version
|Appears in Collections:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.