Improve the Stability and Robustness of Power Management through Model-free Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Improve the Stability and Robustness of Power Management through Model-free Deep Reinforcement Learning

Published:2022-03-14 Issue: Volume: Page:
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Container-title:2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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Author:

Chen Lin¹,Li Xiao¹,Xu Jiang²

Affiliation:

1. The Hong Kong University of Science and Technology,Department of Electronic and Computer Engineering

2. Microelectronics Thrust, The Hong Kong University of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9774496/9774497/09774731.pdf?arnumber=9774731

Reference20 articles.

1. Double Q-learning;hasselt;Advances in neural information processing systems,0

2. Reinforcement learning for control: Performance, stability, and deep approximators;bu?oniu;Annual Reviews in Control,2018

3. Safe model-based reinforcement learning with stability guarantees;berkenkamp;ArXiv Preprint,2017

4. The interplay between stability and regret in online learning;saha;ArXiv Preprint,2012

5. A deeper look at experience replay;zhang;ArXiv Preprint,2017

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Reinforcement Learning-Based Power Management for Chiplet-Based Multicore Systems;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024-09

2. Uncertainty-Aware Online Learning for Dynamic Power Management in Large Manycore Systems;2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED);2023-08-07

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