Pinned Repositories
Code
context-encoder-pytorch
PyTorch implementation of [Context Encoders: Feature Learning by Inpainting
context_encoder_pytorch
PyTorch Implement of Context Encoders: Feature Learning by Inpainting
deep-reinforcement-learning
Repo for the Deep Reinforcement Learning Nanodegree program
DRL-MEC
Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing
DROO
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
DynamicMultiChannelRL
Contains Implementation of Paper " S Wang, H Liu, P H Gomes, and B Krishnamachari ; Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks"
marl-ae-comm
PyTorch implementation for all models and environments in the paper "Learning to Ground Multi-Agent Communication with Autoencoders"
on-policy
This is the official implementation of Multi-Agent PPO (MAPPO).
Youtube-Code-Repository
Repository for most of the code from my YouTube channel
SalwaMostafa's Repositories
SalwaMostafa/DRL-MEC
Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing
SalwaMostafa/DROO
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
SalwaMostafa/marl-ae-comm
PyTorch implementation for all models and environments in the paper "Learning to Ground Multi-Agent Communication with Autoencoders"
SalwaMostafa/on-policy
This is the official implementation of Multi-Agent PPO (MAPPO).
SalwaMostafa/Youtube-Code-Repository
Repository for most of the code from my YouTube channel
SalwaMostafa/Code
SalwaMostafa/context-encoder-pytorch
PyTorch implementation of [Context Encoders: Feature Learning by Inpainting
SalwaMostafa/context_encoder_pytorch
PyTorch Implement of Context Encoders: Feature Learning by Inpainting
SalwaMostafa/deep-reinforcement-learning
Repo for the Deep Reinforcement Learning Nanodegree program
SalwaMostafa/DynamicMultiChannelRL
Contains Implementation of Paper " S Wang, H Liu, P H Gomes, and B Krishnamachari ; Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks"
SalwaMostafa/emergent-language
An implementation of Emergence of Grounded Compositional Language in Multi-Agent Populations by Igor Mordatch and Pieter Abbeel
SalwaMostafa/epymarl
An extension of the PyMARL codebase that includes additional algorithms and environment support
SalwaMostafa/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
SalwaMostafa/From-0-to-Research-Scientist-resources-guide
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
SalwaMostafa/GTG
Source code of "Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning" (AAMAS 2021).
SalwaMostafa/IC3Net
Code for ICLR 2019 paper: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
SalwaMostafa/INFO-F409-EXAM-PROJECT
Project inspired from paper "Learning to cooperate: Emergent communication in multi-agent navigation"
SalwaMostafa/LossLeaP
Loss Learning Predictor for regression problems
SalwaMostafa/MAGIC
Public implementation of "Multi-Agent Graph-Attention Communication and Teaming" from AAMAS'21
SalwaMostafa/Minigrid
Simple and easily configurable grid world environments for reinforcement learning
SalwaMostafa/mobile-env
An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
SalwaMostafa/nassim
Data repository of NAssim
SalwaMostafa/powerful-gnns
How Powerful are Graph Neural Networks?
SalwaMostafa/RAG
Retrieval Augmented Generation with RAG
SalwaMostafa/region-quadtree
A region quadtree used specifically for image compression.
SalwaMostafa/rrm-slice-rl
Code containing RRM simulation using RL in a scenario with RAN slicing.
SalwaMostafa/salwamostafa.github.io
SalwaMostafa/UCMEC_COMMAG
Simulation code and mathematic details of our paper in IEEE Communications Magazine --- WHEN USER-CENTRIC NETWORK MEETS MOBILE EDGE COMPUTING:CHALLENGES AND OPTIMIZATION