Reinforcement learning for spin torque oscillator tasks
This work addresses synchronization challenges in spintronic devices, but it is incremental as it applies existing RL methods to a new domain.
The researchers tackled the problem of automatic synchronization of spintronic oscillators using reinforcement learning, achieving improvements in convergence and energy efficiency in simulated tasks.
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.