用强化学习自动同步自旋电子振荡器，提升效率与收敛速度。
Reinforcement learning for spin torque oscillator tasks
- 基于强化学习控制自旋转移力矩振荡器，实现频率同步
- 在固定步数内完成同步，收敛性与能效均显著提升
- 适用于自旋电子器件自动化调控，适合硬件加速研究者
我们通过强化学习（RL）解决自旋电子振荡器（STO）的自动同步问题。利用宏观自旋朗道-利夫希茨-吉尔伯特-斯隆采夫斯基方程的数值解模拟STO，并训练两类RL智能体，在固定步数内使振荡器同步至目标频率。我们对基础任务进行改进，证明在仿真环境中可轻松实现同步过程的收敛性与能量效率提升。
原文摘要 · Abstract (English)
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.
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