🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Language Model Society (NeruIPS'2023) https://www.camel-ai.org
-
Updated
Jun 18, 2024 - Python
🐫 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.
A fast and lightweight framework for creating decentralized agents with ease.
Harness LLMs with Multi-Agent Programming
[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.
🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX
Let's reproduce paper simulations of multi-robot systems, formation control, distributed optimization and cooperative manipulation.
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch
Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment
A framework for autonomous economic agent (AEA) development
(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.
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.
OpenMAS is an open source multi-agent simulator based in Matlab for the simulation of decentralized intelligent systems defined by arbitrary behaviours and dynamics.
Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention to detail.
This repo contains documentation for public Fetch.ai products.
Lightweight multi-agent gridworld Gym environment
Implementation of Optimal Auctions through Deep Learning
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
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
Simulation for planar area coverage by a swarm of UAVs
Add a description, image, and links to the multi-agent-systems topic page so that developers can more easily learn about it.
To associate your repository with the multi-agent-systems topic, visit your repo's landing page and select "manage topics."