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"Distributed GAN: Toward a Faster Reinforcement-Learning-Based Architec" by Jiachen Shi, Yi Fan et al.

In the existing reinforcement learning (RL)-based neural architecture search (NAS) methods for a generative adversarial network (GAN), both the generator and the discriminator architecture are usually treated as the search objects. In this article, we take a different perspective to propose an approach by treating the generator as the search objective and the discriminator as the judge to evaluate the performance of the generator architecture. Consequently, we can convert this NAS problem to a G...
Generative Adversarial Network Gan Eural Architecture Search Nas Einforcement Learning Rl Eward Shaping
Source: uow.edu.au

"Learning to Charge RF-Energy Harvesting Devices in WiFi Networks" by Yizhou Luo and Kwan Wu Chin

Future WiFi networks will be powered by renewable sources. They will also have radio frequency (RF)-energy harvesting devices. In these networks, a solar-powered access point (AP) will be tasked with supporting both nonenergy harvesting or legacy data users such as laptops, and RF-energy harvesting sensor devices. A key issue is ensuring the AP uses its harvested energy efficiently. To this end, this article contributes two novel solutions that allow the AP to control its transmit power to meet ...
Receding Horizon Control Einforcement Learning Rl Signal To Noise Ratio Task Analysis Wireless Fidelity கேளுங்கள் பகுப்பாய்வு
Source: uow.edu.au

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