  Reinforcement Learning has yielded promising results for Neural Architecture
Search (NAS). In this paper, we demonstrate how its performance can be improved
by using a simplified Transformer block to model the policy network. The
simplified Transformer uses a 2-stream attention-based mechanism to model
hyper-parameter dependencies while avoiding layer normalization and position
encoding. We posit that this parsimonious design balances model complexity
against expressiveness, making it suitable for discovering optimal
architectures in high-dimensional search spaces with limited exploration
budgets. We demonstrate how the algorithm's performance can be further improved
by a) using an actor-critic style algorithm instead of plain vanilla policy
gradient and b) ensembling Transformer blocks with shared parameters, each
block conditioned on a different auto-regressive factorization order. Our
algorithm works well as both a NAS and generic hyper-parameter optimization
(HPO) algorithm: it outperformed most algorithms on NAS-Bench-101, a public
data-set for benchmarking NAS algorithms. In particular, it outperformed RL
based methods that use alternate architectures to model the policy network,
underlining the value of using attention-based networks in this setting. As a
generic HPO algorithm, it outperformed Random Search in discovering more
accurate multi-layer perceptron model architectures across 2 regression tasks.
We have adhered to guidelines listed in Lindauer and Hutter while designing
experiments and reporting results.
