Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures

Leonardo Galliano; Riccardo Rende; Daniele Coslovich

doi:10.1063/5.0221221

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arXiv · 2407.03275

Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures

Leonardo Galliano·Riccardo Rende·Daniele Coslovich

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Abstract

Policy-guided Monte Carlo is an adaptive method to simulate classical interacting systems. It adjusts the proposal distribution of the Metropolis-Hastings algorithm to maximize the sampling efficiency, using a formalism inspired by reinforcement learning. In this work, we first extend the policy-guided method to deal with a general state space, comprising, for instance, both discrete and continuous degrees of freedom, and then apply it to a few paradigmatic models of glass-forming mixtures. We assess the efficiency of a set of physically inspired moves whose proposal distributions are optimized through on-policy learning. Compared to conventional Monte Carlo methods, the optimized proposals are two orders of magnitude faster for an additive soft sphere mixture but yield a much more limited speed-up for the well-studied Kob-Andersen model. We discuss the current limitations of the method and suggest possible ways to improve it.

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Leonardo Galliano, Riccardo Rende, Daniele Coslovich. 2024-07-03. Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures. https://doi.org/10.1063/5.0221221

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