Reinforcement Learning Based Adaptive Energy Management on Diverse Applications for Embedded System
نویسندگان
چکیده
Use of adaptive energy management strategies is essential in improving energy utilization, efficiency, and sustainable operation of embedded systems. Accordingly, this paper presents embedded system applications, which are suitable for employing adaptive energy management utilized reinforcement learning. We proposed rewarding function for embedded system applications in stimulating the learning agent to select the best strategy by learning from environment the agent situated. The proposed adaptive energy management applications are for battery-aware embedded systems of energy harvest wireless sensor network and human-electric hybrid bicycle. Future work of the extended study will focus on applying the reinforcement learning based adaptive energy management for hybrid electric vehicle, and designing a unified rewarding function for diverse applications. Keywordsreinforcement learning; adaptive energy manegement; embeded system; wirless sensor network; human-electric hybrid bicycle; hybrid electric vehicle
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