Low Complexity and Scalable Architecture of Dual-Mode Training and Inference Hardware Accelerator for Deep Q-Network (DQN) Based Edge Computing

Reinforcement Learning (RL) has shown great potential in a wide range of applications, including robotics, autonomous vehicles, and communication systems. However, deploying RL on edge devices poses significant challenges due to limited computational resources and the complexity of real-time decisio...

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Autors principals:	A. Sumarudin, Nana Sutisna, Infall Syafalni, Bambang Riyanto Trilaksono, Trio Adiono
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2025-01-01
Col·lecció:	IEEE Access
Matèries:	AI accelerator deep Q-network (DQN) edge computing FPGA reinforcement learning
Accés en línia:	https://ieeexplore.ieee.org/document/10971177/
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Low Complexity and Scalable Architecture of Dual-Mode Training and Inference Hardware Accelerator for Deep Q-Network (DQN) Based Edge Computing

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