🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Language Model Society (NeruIPS'2023) https://www.camel-ai.org

Python implementation of a bunch of multi-robot path-planning algorithms.

Harness LLMs with Multi-Agent Programming

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

(JAIR'2022) A mini-scale reproduction code of the AlphaStar program. Note: the original AlphaStar is the AI proposed by DeepMind to play StarCraft II. JAIR = Journal of Artificial Intelligence Research.

AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments

Let's reproduce paper simulations of multi-robot systems, formation control, distributed optimization and cooperative manipulation.

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

VMAS is a vectorized framework 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.

A framework for autonomous economic agent (AEA) development

Lightweight multi-agent gridworld Gym environment

灰狼优化算法(GWO)路径规划/轨迹规划/轨迹优化、多智能体/多无人机航迹规划

A zeroconf peer-to-peer mesh VPN using Wireguard® and Interactive Connectivity Establishment (ICE)

OpenMAS is an open source multi-agent simulator based in Matlab for the simulation of decentralized intelligent systems defined by arbitrary behaviours and dynamics.

LUI: Autonomous Collective Decision Making via Large Language Models

Implementation of Optimal Auctions through Deep Learning

RTEC is an Event Calculus implementation optimised for stream reasoning

PowerGridworld provides users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). https://arxiv.org/abs/2111.05969