Show Thoughts

SOMs can be used for preprocessing in reinforcement learning, simplifying their high-dimensional input via their winner-take-all characteristics.

However, since standard SOMs do not get any goal-dependent input, they focus on globally strongest features (statistically most predictive latent variables) and under-emphasize features which would be relevant for the task.