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• W2889345494 abstract "We introduce policy-guided Monte Carlo (PGMC), a computational framework using reinforcement learning to improve Markov chain Monte Carlo (MCMC) sampling. The methodology is generally applicable, unbiased, and opens up a path to automated discovery of efficient MCMC samplers. After developing a general theory, we demonstrate some of PGMC's prospects on an Ising model on the kagome lattice, including when the model is in its computationally challenging kagome spin ice regime. Here we show that PGMC is able to automatically machine learn efficient MCMC updates without a priori knowledge of the physics at hand." @default.
• W2889345494 created "2018-09-07" @default.
• W2889345494 creator A5069409709 @default.
• W2889345494 date "2018-12-04" @default.
• W2889345494 modified "2023-10-12" @default.
• W2889345494 title "Policy-guided Monte Carlo: Reinforcement-learning Markov chain dynamics" @default.
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• W2889345494 doi "https://doi.org/10.1103/physreve.98.063303" @default.
• W2889345494 hasPublicationYear "2018" @default.
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