Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917755030077440 |
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| author | Galliano, Leonardo Rende, Riccardo Coslovich, Daniele |
| author_facet | Galliano, Leonardo Rende, Riccardo Coslovich, Daniele |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_03275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures Galliano, Leonardo Rende, Riccardo Coslovich, Daniele Soft Condensed Matter Statistical Mechanics 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. |
| title | Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures |
| topic | Soft Condensed Matter Statistical Mechanics |
| url | https://arxiv.org/abs/2407.03275 |