Yi Wei
Shandong University
12 Papers
4 Citations
Yi Wei is an academic researcher from Shandong University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 4, co-authored 10 publications.
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Papers
DRL-Scheduling: An Intelligent QoS-Aware Job Scheduling Framework for Applications in Clouds
TL;DR: A deep reinforcement learning-based job scheduler is the key component of the framework, able to learn to make appropriate online job-to-VM decisions for continuous job requests directly from its experiences without any prior knowledge.
85
A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment
Yi Wei,Daniel Kudenko,Daniel Kudenko,Shijun Liu,Li Pan,Lei Wu,Xiangxu Meng +6 more
- 11 Dec 2017
TL;DR: This paper regards the service composition problem as a sequential decision making process and solves it by means of reinforcement learning, and demonstrates that this approach can find near-optimal solutions through continuous learning in the dynamic cloud market.
13
Study on Design and Diamond Turning of Optical Freeform Surface for Progressive Addition Lenses
TL;DR: An optimized tool path generation method for diamond turning of the optical freeform surface is proposed, the equal angle method is used to select the discrete points, and a tool nose radius compensation method suitable for both slow slide servo (SSS) and fast tool Servo (FTS) is adopted.
Data fusing and joint training for learning with noisy labels
TL;DR: This paper proposes a new method for selecting training data accurately and fits a mixture model to the per-sample loss of the raw label and the predicted label, and the mixture model is utilized to dynamically divide the training set into a correctly labeled set, a correctly predicted set, and a wrong set.
7
Learning manipulation skills with demonstrations for the swing process control of dredgers
TL;DR: In this paper , a learning approach for the intelligent control of the swing process of a CSD so as to release human operators from such a boring and heavy task is presented, which is formulated as a sequential decision making problem, and Deep Reinforcement Learning (DRL) is employed to design the learning approach based on deterministic policy gradient.