liuningsuper Goto Github PK

advanced-tdma-ns3-simulation

architect-awesome

后端架构师技术图谱

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

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。

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

db-monthly

阿里云数据库内核月报分类整理（定时更新）。

deep-learning-with-pytorch-tutorials

深度学习与PyTorch入门实战视频教程

diral

Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V Communication

drl

Deep Reinforcement Learning

easy-rl

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

efficient-motion-planning

To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles

elegantrl

Scalable and Elastic Deep Reinforcement Learning Using PyTorch. Please star. 🔥

fedml

A Research-oriented Federated Learning Library. Supporting distributed computing, mobile/IoT on-device training, and standalone simulation. Best Paper Award at NeurIPS 2020 Federated Learning workshop. Join our Slack Community:(https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w)

frp

A fast reverse proxy to help you expose a local server behind a NAT or firewall to the internet.

guava

Google core libraries for Java

hangzhou_house_knowledge

2017年买房经历总结出来的买房购房知识分享给大家，希望对大家有所帮助。买房不易，且买且珍惜。Sharing the knowledge of buy an own house that according to the experience at hangzhou in 2017 to all the people. It's not easy to buy a own house, so I hope that it would be useful to for everyone.

hexo-theme-matery

A beautiful hexo blog theme with material design and responsive design.一个基于材料设计和响应式设计而成的全面、美观的Hexo主题。国内访问：http://blinkfox.com

leader-selection-simulation

A SUMO environment for verifying leader selection algorithms with noisy DSRC model

learning-based-queuing-delay-aware-task-offloading-in-collaborative-vehicular-networks

Evoluationay Algorithms Project: Learning-Based Queuing Delay-Aware Task Offloading in Collaborative Vehicular Networks

leetcodetop

汇总各大互联网公司容易考察的高频leetcode题🔥

liuningsuper.github.io

lshort-zh-cn

A Chi­nese edi­tion of the Not So Short Introduction to LaTeX2ε

marlspectrumsharingv2x

Spectrum sharing in vehicular networks based on multi-agent reinforcement learning, IEEE Journal on Selected Areas in Communications

mec_drl

Deep reinforcement learning for mobile edge computing

mininet

Emulator for rapid prototyping of Software Defined Networks

ns3-sumo-coupling

A ns3 module for bidirectional coupling with SUMO

openapi-specification

The OpenAPI Specification Repository

pdf

编程电子书，电子书，编程书籍，包括C，C#，Docker，Elasticsearch，Git，Hadoop，HeadFirst，Java，Javascript，jvm，Kafka，Linux，Maven，MongoDB，MyBatis，MySQL，Netty，Nginx，Python，RabbitMQ，Redis，Scala，Solr，Spark，Spring，SpringBoot，SpringCloud，TCPIP，Tomcat，Zookeeper，人工智能，大数据类，并发编程，数据库类，数据挖掘，新面试题，架构设计，算法系列，计算机类，设计模式，软件测试，重构优化，等更多分类

pytorch-a2c-ppo-acktr-gail

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

rsshub