ZijieHuang's Stars
transys-project/metis
Interpreting Deep Learning-Based Networking Systems (SIGCOMM 2020)
mfarreras/gain-gnn
jwwthu/GNN-Communication-Networks
This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
yshenaw/GNN4Com
BNN-UPC/NetworkModelingDatasets
This repository contains datasets for network modeling simulated with OMNet++
zamaniali1995/RL-4-SFC-mapping
simulation of "A novel reinforcement learning algorithm for virtual network emb e dding" paper
CN-UPB/NFVdeep
NFVdeep: Deep Reinforcement Learning for Online Orchestration of Service Function Chains
AlexPasqua/DeepNetSlice
Reinforcement Learning tool for Network Slice Placement problems
yule-BUAA/R-HGNN
codes of R-HGNN model for Heterogeneous Graph Representation Learning
stellargraph/stellargraph
StellarGraph - Machine Learning on Graphs
GeminiLight/virne
Virne is a simulator for resource allocation problems in network virtualization, mainly for virtual network embedding (VNE). It also is adaptable to VNE's variants, such as service function chain deployment (SFC Deployment), network slicing, etc.
ZGCTroy/Pointer_Network
code for "Modeling on virtual network embedding using reinforcement learning"
Jhy1993/HAN
Heterogeneous Graph Neural Network
williamleif/GraphSAGE
Representation learning on large graphs using stochastic graph convolutions.
avnavarretes/bias_mitigation
cerob/slicesim
5G Network Slicing Simulation
Ms-Wang01/Virtual-Network-Embedding
Virtual Network Embedding Environment for Reinforcement Learning written in python
mneedham/london-buses
Playing around the London Buses API
buurenzo/ipyleafletdemo
MichaelAllen1966/qambo
Experiments in Deep Q Learning controlling ambulance placement
fladdimir/casymda
Discrete-Event-Simulation based on BPMN and SimPy
karlapalem/UC-Berkeley-AI-Pacman-Project
Artificial Intelligence project designed by UC Berkeley. Designed game agents for the game Pacman using basic, adversarial and stochastic search algorithms, and reinforcement learning concepts
jupadhya1/REINFORCEMENT-LEARNING
Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.
co-cddo/algorithmic-transparency-recording-standard
RunzheYang/MORL
Multi-Objective Reinforcement Learning
lexeree/normative-player-characters
mlpeschl/moral_rl
axelabels/DynMORL
Code for Dynamic Weights in Multi-Objective Deep Reinforcement Learning
kristery/EthicsShaping
[AAAI 2018] Implementation of the Ethics Shaping approach proposed in "A low-cost ethics shaping approach for designing reinforcement learning agents"
lrhammond/lmorl
Lexicographic Multi-Objective Reinforcement Learning