  Reinforcement learning (RL) is a goal-oriented learning solution that has
proven to be successful for Neural Architecture Search (NAS) on the CIFAR and
ImageNet datasets. However, a limitation of this approach is its high
computational cost, making it unfeasible to replay it on other datasets.
Through meta-learning, we could bring this cost down by adapting previously
learned policies instead of learning them from scratch. In this work, we
propose a deep meta-RL algorithm that learns an adaptive policy over a set of
environments, making it possible to transfer it to previously unseen tasks. The
algorithm was applied to various proof-of-concept environments in the past, but
we adapt it to the NAS problem. We empirically investigate the agent's behavior
during training when challenged to design chain-structured neural architectures
for three datasets with increasing levels of hardness, to later fix the policy
and evaluate it on two unseen datasets of different difficulty. Our results
show that, under resource constraints, the agent effectively adapts its
strategy during training to design better architectures than the ones designed
by a standard RL algorithm, and can design good architectures during the
evaluation on previously unseen environments. We also provide guidelines on the
applicability of our framework in a more complex NAS setting by studying the
progress of the agent when challenged to design multi-branch architectures.
