Towards neural reinforcement learning for large deviations in nonequilibrium systems with memory

Venkata D. Pamulaparthy; Rosemary J. Harris

doi:10.1088/1742-5468/adea65

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arXiv · 2501.12333

Towards neural reinforcement learning for large deviations in nonequilibrium systems with memory

Venkata D. Pamulaparthy·Rosemary J. Harris

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Abstract

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.

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Venkata D. Pamulaparthy, Rosemary J. Harris. 2025-01-21. Towards neural reinforcement learning for large deviations in nonequilibrium systems with memory. https://doi.org/10.1088/1742-5468%2Fadea65

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