Chong Lv's starred repositories
mec-with-ris-control
Codes for reproducing the numerical results reported in: "Control Aspects for Using RIS in Latency-Constrained Mobile Edge Computing" by F. Saggese, V. Croisfelt, F. Costanzo, J. Shiraishi, R. Kotaba, P. Di Lorenzo, and P. Popovski.
sns3-satellite
Satellite module for ns-3 simulator
Deep-learning-with-Python
Example projects I completed to understand Deep Learning techniques with Tensorflow.
Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
STKCodeExamples
Example scripts and applications for automating and developing with STK and STK Engine.
Interactive-Multi-objective-Reinforcement-Learning
Multi-objective reinforcement learning deals with finding policies for tasks where there are multiple distinct criteria to optimize for. Since there may be trade-offs between the criteria, there does not necessarily exist a globally best policy; instead, the goal is to find Pareto optimal policies that are the best for certain preference functions. The Pareto Q-learning algorithm looks for all Pareto optimal policies at the same time. Introduced a variant of Pareto Q-learning that asks queries to a user, who is assumed to have an underlying preference function and also the scalarized Q-learning algorithm which reduces the dimensionality of multi-objective space by using scalarization function and ask user preferences by taking weights for scalarization. The goal is to find the optimal policy for that user’s preference function as quickly as possible. Used two benchmark problems i.e. Deep Sea Treasure and Resource Collection for experiments.
sns3-satellite
Satellite module for ns-3 simulator
pumpkin-book
《机器学习》（西瓜书）公式详解
python-skyfield
Elegant astronomy for Python