🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
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Updated
Jun 7, 2024 - Python
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
A Platform for Many-Agent Reinforcement Learning
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
Spising: ⚡️Open-source AI LangChain-like RAG (Retrieval-Augmented Generation) knowledge database with web UI and Enterprise SSO⚡️, supports OpenAI, Azure, LLaMA, Google Gemini, HuggingFace, Claude, Grok, etc., chat bot demo: https://demo.casibase.com, admin UI demo: https://demo-admin.casibase.com
This is the official implementation of Multi-Agent PPO (MAPPO).
Start building LLM-empowered multi-agent applications in an easier way.
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
Awesome Game AI materials of Multi-Agent Reinforcement Learning
A collection of multi agent environments based on OpenAI gym.
An intelligent assistant serving the entire software development lifecycle, powered by a Multi-Agent Framework, working with DevOps Toolkits, Code&Doc Repo RAG, etc.
Trajectory Planner in Multi-Agent and Dynamic Environments
Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment
API to run VirtualHome, a Multi-Agent Household Simulator
Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving
A framework for autonomous economic agent (AEA) development
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.
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