Controlling dynamics of stochastic systems with deep reinforcement learning
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917935894757376 |
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| author | Mukhamadiarov, Ruslan |
| author_facet | Mukhamadiarov, Ruslan |
| contents | A properly designed controller can help improve the quality of experimental measurements or force a dynamical system to follow a completely new time-evolution path. Recent developments in deep reinforcement learning have made steep advances toward designing effective control schemes for fairly complex systems. However, a general simulation scheme that employs deep reinforcement learning for exerting control in stochastic systems is yet to be established. In this paper, we attempt to further bridge a gap between control theory and deep reinforcement learning by proposing a simulation algorithm that allows achieving control of the dynamics of stochastic systems through the use of trained artificial neural networks. Specifically, we use agent-based simulations where the neural network plays the role of the controller that drives local state-to-state transitions. We demonstrate the workflow and the effectiveness of the proposed control methods by considering the following two stochastic processes: particle coalescence on a lattice and a totally asymmetric exclusion process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18111 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Controlling dynamics of stochastic systems with deep reinforcement learning Mukhamadiarov, Ruslan Statistical Mechanics Machine Learning Computational Physics A properly designed controller can help improve the quality of experimental measurements or force a dynamical system to follow a completely new time-evolution path. Recent developments in deep reinforcement learning have made steep advances toward designing effective control schemes for fairly complex systems. However, a general simulation scheme that employs deep reinforcement learning for exerting control in stochastic systems is yet to be established. In this paper, we attempt to further bridge a gap between control theory and deep reinforcement learning by proposing a simulation algorithm that allows achieving control of the dynamics of stochastic systems through the use of trained artificial neural networks. Specifically, we use agent-based simulations where the neural network plays the role of the controller that drives local state-to-state transitions. We demonstrate the workflow and the effectiveness of the proposed control methods by considering the following two stochastic processes: particle coalescence on a lattice and a totally asymmetric exclusion process. |
| title | Controlling dynamics of stochastic systems with deep reinforcement learning |
| topic | Statistical Mechanics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2502.18111 |