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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-lifeintelligent devices, while the prohibitive complexity of DRL stands at oddswith limited on-device resources. In this work, we propose an Automated AgentAccelerator Co-Search (A3C-S) framework, which to our best knowledge is thefirst to automatically co-search the optimally matched DRL agents andaccelerators that maximize both test scores and hardware efficiency. Extensiveexperiments consistently validate the superiority of our A3C-S overstate-of-the-art techniques.

A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning