  Deployment efficiency is an important criterion for many real-world
applications of reinforcement learning (RL). Despite the community's increasing
interest, there lacks a formal theoretical formulation for the problem. In this
paper, we propose such a formulation for deployment-efficient RL (DE-RL) from
an "optimization with constraints" perspective: we are interested in exploring
an MDP and obtaining a near-optimal policy within minimal \emph{deployment
complexity}, whereas in each deployment the policy can sample a large batch of
data. Using finite-horizon linear MDPs as a concrete structural model, we
reveal the fundamental limit in achieving deployment efficiency by establishing
information-theoretic lower bounds, and provide algorithms that achieve the
optimal deployment efficiency. Moreover, our formulation for DE-RL is flexible
and can serve as a building block for other practically relevant settings; we
give "Safe DE-RL" and "Sample-Efficient DE-RL" as two examples, which may be
worth future investigation.
