multi-agent-reinforcement-learning

An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II

ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.

Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).

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

Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

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

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.

Unified Reinforcement Learning Framework

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.

A collection of MARL benchmarks based on TorchRL

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

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

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:

A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

📚 List of Top-tier Conference Papers on Reinforcement Learning (RL)，including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.

DI-engine docs (Chinese and English)