  Driven by the explosive interest in applying deep reinforcement learning
(DRL) agents to numerous real-time control and decision-making applications,
there has been a growing demand to deploy DRL agents to empower daily-life
intelligent devices, while the prohibitive complexity of DRL stands at odds
with limited on-device resources. In this work, we propose an Automated Agent
Accelerator Co-Search (A3C-S) framework, which to our best knowledge is the
first to automatically co-search the optimally matched DRL agents and
accelerators that maximize both test scores and hardware efficiency. Extensive
experiments consistently validate the superiority of our A3C-S over
state-of-the-art techniques.
