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arXiv · 2106.06577

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

Yonggan Fu·Yongan Zhang·Chaojian Li·Zhongzhi Yu·Yingyan Celine Lin
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Abstract

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.

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Yonggan Fu, Yongan Zhang, Chaojian Li, Zhongzhi Yu, Yingyan Celine Lin. 2021-06-11. A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning. https://arxiv.org/abs/2106.06577

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