Mango.jl
Mango.jl: A Julia-Based Multi-Agent Simulation Framework - Published in JOSS (2024)
marlware
Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment
vmas
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
ReMobidyc
web based multi-agent simulator for individual-based modeling in population dynamics and ecotoxicology
https://github.com/ai4co/camp
[AAMAS 2025 Oral] CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
2023-code-acc-h2-controller-synthesis-for-multi-agent-systems-with-stochastic-packet-loss
Code for the paper "Distributed H2 Controller Synthesis for Multi-Agent Systems with Stochastic Packet Loss" by C. Hespe, A. Datar, D. Schneider, H. Saadabadi, H. Werner and H. Frey
https://github.com/ai4co/eph-mapf
[IROS'24] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
macro
[ICC '21] MACRO: Megastructure Assembly via Collaborative Robots in Orbits
forex-trading-automation-with-deep-reinforcement-learning
Forex Trading Automation with PPO, ACKTR, DDPG, TD3 and Ensemble Strategy
https://github.com/arbit3rr/flocking-multi-agent
Python implementation of "Flocking for multi-agent dynamic systems: Algorithms and theory" by Olfati-Saber for multi-agent triangular formation.
https://github.com/fetchai/docs-archived
This repo contains documentation for public Fetch.ai products.
TensorGames
Computing mixed-strategy Nash Equilibria for games involving multiple players
2023-code-ifac-performance-analysis-for-time-varying-mas-with-packet-loss
Code for the paper "Robust Performance Analysis for Time-Varying Multi-Agent Systems with Stochastic Packet Loss" by C. Hespe and H. Werner
2022-code-tcns-decomposition-approach-to-multi-agent-systems-with-bernoulli-packet-loss
Code for the paper "A Decomposition Approach to Multi-Agent Systems with Bernoulli Packet Loss" by C. Hespe, H. Saadabadi, A. Datar, H. Werner and Y. Tang
hashiru
The Expert Orchestrator AI: Dynamically Adapting, Budget-Aware, and Precisely Tailored to Your Needs
quantum-swarm-path-planning
This repository contains the code that implements the procedure proposed in the paper "Quantum planning for swarm robotics" by A. Chella, S. Gaglio, M. Mannone, G. Pilato, V. Seidita, F. Vella, and S. Zammuto.
heterogeneous-multi-agent-systems
Research project about heterogeneous multi-agent systems.