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a3c_grid_world

Simple tensorflow implementation of Asynchronous Advantage Actor-Critic (A3C) for a 2-D grid environment

aqua-sim-ng

Aqua-Sim on NS3

audeep

bo_pr

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization

collaq

A code implementation for our arXiv paper "Multi-agent Adhoc Team Play using Decompositional Q function"

competitive-cell-free

Simulation code for “Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation,” by Emil Björnson and Luca Sanguinetti, IEEE Transactions on Wireless Communications, vol. 19, no. 1, pp. 77-90, January 2020

ddpg-aigym

Continuous control with deep reinforcement learning - Deep Deterministic Policy Gradient (DDPG) algorithm implemented in OpenAI Gym environments

deep-reinforcement-learning-for-5g-networks

Code for my publication: Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination. Paper accepted for publication to IEEE Transactions on Communications.

deepcomp

Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning

deeprl-agents

A set of Deep Reinforcement Learning Agents implemented in Tensorflow for Grid World

denoise-autoencoder

Denoise audio with convolutional autoencoder

epymarl

An extension of the PyMARL codebase that includes additional algorithms and environment support

federated

A framework for implementing federated learning

griffin_lim

Implementation of the Griffin and Lim algorithm to recover an audio signal from a magnitude-only spectrogram.

gym-minigrid

Minimalistic gridworld package for OpenAI Gym

hands-on-vision-and-behavior-for-self-driving-cars

Hands-On Vision and Behavior for Self-Driving Cars, published by Packt

harl

Official implementation of HARL algorithms based on PyTorch.

highway-env

A minimalist environment for decision-making in autonomous driving

irs-relaying

Simulation code for “Intelligent Reflecting Surface vs. Decode-and-Forward: How Large Surfaces Are Needed to Beat Relaying?,” by Emil Björnson, Özgecan Özdogan, Erik G. Larsson, IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 244-248, February 2020.

lec-smartmobileplatform

ltev2vsim

LTEV2Vsim V2X network simulator

marl-patrolling-agents

Project on multi agent reinforcement learning applied on patrolling agents

marlspectrumsharingv2x

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

metacar

A reinforcement learning environment for self-driving cars in the browser.

mmnet

mmwave-energy

MmWave UE Energy Module for ns3

mobile-env

An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.

multi-agent-deep-deterministic-policy-gradients

A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

multi-agent-deep-deterministic-policy-gradients-pytorch

Multi-Agent-Deep-Deterministic-Policy-Gradients-PyTorch

multi-agent-reinforcement-learning

Solving the ma-gym (multi-agent version of OpenAI gym) game Switch with 2 and 4 agents using DQN and PPO.