OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

Reinforcement learning tutorials

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

Datasets and code for machine learning in 5G mmWave MIMO systems involving mobility (5GMdata)

Literature reading group / ITN WindMill / https://windmill-itn.eu/

multi-agent deep reinforcement learning for networked system control.

A deep reinforcement learning based approach is used to allocate downlink power for multi-cell wireless system.

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

Code repository for On the interaction between supervision and self-play in emergent communication (ICLR 2020)

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

Deep Reinforcement Learning Based Dynamic Resource Allocation in 5G Ultra-Dense Networks

Minimal Monte Carlo Policy Gradient (REINFORCE) Algorithm Implementation in Keras

无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

Code for the paper "Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels"

Python Implementation of Reinforcement Learning: An Introduction

MIMO Env using Reinforcement Learning

Using multi-agent Deep Q Learning with LSTM cells (DRQN) to train multiple users in cognitive radio to learn to share scarce resource (channels) equally without communication

Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

Implementation of Irregular Repetition Slotted ALOHA protocol.

Has Aloha Simulation in it

Implementation of research paper: End-to-End Learning of Communications Systems Without a Channel Model

Contains Implementation of Paper " S Wang, H Liu, P H Gomes, and B Krishnamachari ; Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks"

Machine Learning Applications in Wireless Communications - Project work

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

Reinforcement learning based scheduling algorithm for optimizing AoI in URLLC networks

Depicts autoencoders as end-to-end communication systems