In this chapter, we have implemented NAS, a framework where a reinforcement learning agent (the Controller) generates child neural networks to complete a certain task. We studied the theory behind how the Controller learns to generate better child network architectures via policy gradient methods. We then implemented a simplified version of NAS that generates child networks that learn to classify CIFAR-10 images.

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