Reinforcement learning for spin torque oscillator tasks
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912584733556736 |
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| author | Mojsiejuk, Jakub Ziętek, Sławomir Skowroński, Witold |
| author_facet | Mojsiejuk, Jakub Ziętek, Sławomir Skowroński, Witold |
| contents | We address the problem of automatic synchronisation of the spintronic oscillator (STO) by means of reinforcement learning (RL). A numerical solution of the macrospin Landau-Lifschitz-Gilbert-Slonczewski equation is used to simulate the STO and we train the two types of RL agents to synchronise with a target frequency within a fixed number of steps. We explore modifications to this base task and show an improvement in both convergence and energy efficiency of the synchronisation that can be easily achieved in the simulated environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement learning for spin torque oscillator tasks Mojsiejuk, Jakub Ziętek, Sławomir Skowroński, Witold Applied Physics Artificial Intelligence Machine Learning We address the problem of automatic synchronisation of the spintronic oscillator (STO) by means of reinforcement learning (RL). A numerical solution of the macrospin Landau-Lifschitz-Gilbert-Slonczewski equation is used to simulate the STO and we train the two types of RL agents to synchronise with a target frequency within a fixed number of steps. We explore modifications to this base task and show an improvement in both convergence and energy efficiency of the synchronisation that can be easily achieved in the simulated environment. |
| title | Reinforcement learning for spin torque oscillator tasks |
| topic | Applied Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.10057 |