Dynamic decentralized task allocation algorithms for multi-agent systems using auctions and machine learning

Public implementation of "Multi-Agent Graph-Attention Communication and Teaming" from AAMAS'21

Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

MAGNet: Multi-agents control using Graph Neural Networks

Official repository of the paper TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems (AAMAS 2023)

IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL

Modeling and Simulation of Multicomponent Distillation Columns

Code for the paper “Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning”

Gym Electric Motor (GEM): An OpenAI Gym Environment for Electric Motors

Deep reinforcement learning for design of distillation column trains (chemical engineering process synthesis)

A Multi-Agent Reinforcement Learning Environment for Information Dissemination based on Graph Convolutional Reinforcement Learning

Codes used for "Model-based safe reinforcement learning for nonlinear systems under uncertainty with constraints tightening approach"

WWTP parameter optimization by DDPG

Tools for accelerating safe exploration research.

MATPOWER – steady state power flow simulation and optimization for MATLAB and Octave

Codes for "Quantitative Comparison of Reinforcement Learning and Data-driven Model Predictive Control for Chemical and Biological Processes"

Codes for paper of 'Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning'

Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

Deep reinforcement learning for design of chemical engineering processes

Research repository

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

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

The code of AAAI2021 paper of HGCN for Traffic Forecasting

Code for NeurIPS paper "Self-Organized Group for Cooperative Multi-agentReinforcement Learning".

multi-agent deep reinforcement learning for large-scale traffic signal control.

Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:

Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment