lc837948166

lc837948166's Stars

• fengdu78/Coursera-ML-AndrewNg-Notes

  吴恩达老师的机器学习课程个人笔记

  Language:HTML33.3k1k6810.8k

• coder2gwy/coder2gwy

  互联网首份程序员考公指南，由3位已经进入体制内的前大厂程序员联合献上。

  26.9k3761543.7k

• datawhalechina/pumpkin-book

  《机器学习》（西瓜书）公式详解

  24.6k623774.8k

• thu-ml/tianshou

  An elegant PyTorch deep reinforcement learning library.

  Language:Python8.3k917591.1k

• tangtangcoding/C-C-

  程序员相关电子书资料免费分享，欢迎关注个人微信公众号：编程与实战

  4.7k90121.2k

• BIMK/PlatEMO

  Evolutionary multi-objective optimization platform

  Language:MATLAB1.8k50112482

• skyfielders/python-skyfield

  Elegant astronomy for Python

  Language:Python1.5k56685223

• poliastro/poliastro

  poliastro - :rocket: Astrodynamics in Python

  Language:Python91525766288

• esa/pykep

  PyKEP is a scientific library providing basic tools for research in interplanetary trajectory design.

  Language:C++360388198

• snkas/hypatia

  Low earth orbit (LEO) satellite network simulation framework.

  Language:C++3351329121

• AnalyticalGraphicsInc/STKCodeExamples

  Example scripts and applications for automating and developing with STK and STK Engine.

  Language:Jupyter Notebook2161614140

• sns3/sns3-satellite

  Satellite module for ns-3 simulator

  Language:C++150114658

• Haoran-Peng/UAV-RIS_EH_DDPG

  Language:Python801223

• kshitija2/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.

  Language:Python21008

• victorcroisfelt/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.

  Language:MATLAB12102

• longnehc/hypatia

  Language:C++82

• EthanHYX/sns3-satellite

  Satellite module for ns-3 simulator

  Language:C++1000

• ngchc/Deep-learning-with-Python

  Example projects I completed to understand Deep Learning techniques with Tensorflow.

  Language:Jupyter Notebook1100