  In this paper, we study a best arm identification problem with dual objects.
In addition to the classic reward, each arm is associated with a cost
distribution and the goal is to identify the largest reward arm using the
minimum expected cost. We call it \emph{Cost Aware Best Arm Identification}
(CABAI), which captures the separation of testing and implementation phases in
product development pipelines and models the objective shift between phases,
i.e., cost for testing and reward for implementation. We first derive a
theoretical lower bound for CABAI and propose an algorithm called
$\mathsf{CTAS}$ to match it asymptotically. To reduce the computation of
$\mathsf{CTAS}$, we further propose a simple algorithm called \emph{Chernoff
Overlap} (CO), based on a square-root rule, which we prove is optimal in
simplified two-armed models and generalizes well in numerical experiments. Our
results show that (i) ignoring the heterogeneous action cost results in
sub-optimality in practice, and (ii) simple algorithms can deliver near-optimal
performance over a wide range of problems.
