Importance-Driven Data Collection for Efficient Online Learning Over the Wireless Edge
Author:
Affiliation:
1. Beijing University of Posts and Telecommunications (BUPT),Beijing Key Laboratory of Space-ground Interconnection and Convergence,Beijing,China,100876
2. Carleton University,Department of Systems and Computer Engineering,Ottawa,ON,Canada,K1S 5B6
Funder
National Key R & D Program of China
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278505/10278554/10278679.pdf?arnumber=10278679
Reference13 articles.
1. Human-level control through deep reinforcement learning;mnih;Nature,2015
2. Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation;kulkarni;Proc NIPS 2016,2016
3. Lyapunov-Guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks
4. Stochastic Network Optimization with Application to Communication and Queueing Systems
5. Self-paced learning for latent variable models;kumar;Proc NIPS 2010,2010
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