qmix

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Concise pytorch implements of MARL algorithms, including MAPPO, MADDPG, MATD3, QMIX and VDN.

xingtian is a componentized library for the development and verification of reinforcement learning algorithms

PyTorch implements multi-agent reinforcement learning algorithms, including QMIX, Independent PPO, Centralized PPO, Grid Wise Control, Grid Wise Control+PPO, Grid Wise Control+DDPG.

MARLToolkit: The Multi-Agent Rainforcement Learning Toolkit. Include implementation of MAPPO, MADDPG, QMIX, VDN, COMA, IPPO, QTRAN, MAT...

StarCraft II Multi Agent Challenge : QMIX, COMA, LIIR, QTRAN, Central V, ROMA, RODE, DOP, Graph MIX

PPO and PyMARL baseline for Pogema environment

RLToolkit is a flexible and high-efficient reinforcement learning framework. Include implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Source code for the MARL-enabled real-time control of urban drainage system

General-Purpose Reinforcement Learning

QMIX implemented in TensorFlow 2

Continual Multi-agent Reinforcement Learning in Dynamic Environments

Scalable Multi-Agent Reinforcement Learning with IQL and QMIX — from-scratch implementation in a custom grid environment. Compare independent vs centralized learning as agents scale from 2 to 10.

Modular cooperative multi-agent reinforcement learning with communication, reproducible demos and explicit validation evidence.

A comparison between single agent and multi agent framework for object localization.

JKU Msc POC course project

RAVEN: Receiver-conditioned Action-Value ENcoding for finite-symbol (2-bit) multi-agent communication

Multi-Agent RL HW3 (Grade 97, M.Sc. Data Science, HIT): value decomposition with the textbook codebase — IDQN, VDN and QMIX compared on cooperative Level-Based Foraging and two SMAClite combat scenarios, with rollout videos from early and late training

Integrated supply chain intelligence platform for disruption detection, demand forecasting, resource allocation, and route optimization using GNNs, TFT, MARL, and POMO.