I am looking at this paper
Designing Neural Network Architectures using Reinforcement Learning.
The paper discussed how to find the best network using Reinforcement Learning.
According to APPENDIX A(Page 12):
M refers to number of models. That means, according to action plan (as shown Table 1, Page 5), the number of models to train is fixed.
It may be 1500 number of models or 2000 number of models according to image size.
I am confused for the advantage of using Reinforcement Learning in this condition. I can train all 1500 models and can select manually the best performance model.
Why we need Q Learning strategy and complicated stuffs?