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Reinforcement learning for spin torque oscillator tasks

Created by
  • Haebom

Author

Jakub Mojsiejuk, S{\l}awomir Zi\k{e}tek, Witold Skowro nski

Outline

This paper addresses the problem of automatic synchronization of spintronic oscillators (STOs) using reinforcement learning (RL). We simulate STOs using numerical solutions of the macroscopic spin Landau-Lipschitz-Gilbert-Slonczewski equations and train two types of RL agents to synchronize to a target frequency within a fixed step. We explore modifications to the underlying task and demonstrate that convergence and energy efficiency improvements in synchronization can be readily achieved in a simulation environment.

Takeaways, Limitations

Takeaways:
Presentation and performance verification of a reinforcement learning-based STO synchronization algorithm.
Suggesting the possibility of improving synchronization convergence speed and energy efficiency.
Presenting the possibility of easy implementation in a simulation environment.
Limitations:
Results are limited to a simulation environment. Further research is needed for application to real systems.
Lack of detailed description of the type and specific structure of the RL agent used.
Lack of quantitative assessment of energy efficiency improvements.
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