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Bibliographic Details
Main Authors: Pamulaparthy, Venkata D., Harris, Rosemary J.
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2501.12333
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author Pamulaparthy, Venkata D.
Harris, Rosemary J.
author_facet Pamulaparthy, Venkata D.
Harris, Rosemary J.
contents We introduce a reinforcement learning method for a class of non-Markov systems; our approach extends the actor-critic framework given by Rose et al. [New J. Phys. 23 013013 (2021)] for obtaining scaled cumulant generating functions characterizing the fluctuations. The actor-critic is implemented using neural networks; a particular innovation in our method is the use of an additional neural policy for processing memory variables. We demonstrate results for current fluctuations in various memory-dependent models with special focus on semi-Markov systems where the dynamics is controlled by nonexponential interevent waiting time distributions.
format Preprint
id arxiv_https___arxiv_org_abs_2501_12333
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Towards neural reinforcement learning for large deviations in nonequilibrium systems with memory
Pamulaparthy, Venkata D.
Harris, Rosemary J.
Statistical Mechanics
We introduce a reinforcement learning method for a class of non-Markov systems; our approach extends the actor-critic framework given by Rose et al. [New J. Phys. 23 013013 (2021)] for obtaining scaled cumulant generating functions characterizing the fluctuations. The actor-critic is implemented using neural networks; a particular innovation in our method is the use of an additional neural policy for processing memory variables. We demonstrate results for current fluctuations in various memory-dependent models with special focus on semi-Markov systems where the dynamics is controlled by nonexponential interevent waiting time distributions.
title Towards neural reinforcement learning for large deviations in nonequilibrium systems with memory
topic Statistical Mechanics
url https://arxiv.org/abs/2501.12333