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.

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

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

JKU Msc POC course project

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

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

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

POMDP cat-and-mouse PettingZoo grid world with recurrent MARL baselines, diagnostics, and demo assets.