Automated Machine Learning with scikit-learn

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

This is the official implementation of Multi-Agent PPO (MAPPO).

Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)

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

Unofficial open-source OpenGL/SDL2 reimplementation of Sid Meier's Alpha Centauri ( + Alien Crossfire )

[AAAI 2023] Official PyTorch implementation of paper "ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency".

LLM-PySC2 is NKAI Decision Team and NUDT Decision Team's Python component of the StarCraft II LLM Decision Environment. It exposes Deepmind's PySC2 Learning Environment API as a Python LLM Environment.

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

Multi-agent PPO with noise (97% win rates on Hard scenarios of SMAC)

[ICML 2021] DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

Bayesian Optimization for Categorical and Continuous Inputs

This repository considers the implementation of the paper "FoX: Formation-aware exploration in multi-agent reinforcement learning" which has been accepted to AAAI 2024

Continual Multi-agent Reinforcement Learning in Dynamic Environments

Anti-Cheat Detection for Game Streaming

Clean, documented implementations of PPO-based algorithms for cooperative multi-agent reinforcement learning, focusing on SMAC environments. Features MLP and RNN-based MAPPO with various normalization techniques.

[JMLR 2023] A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

An automated data augmentation tool in Python

This is the official implementation of [AAAI'25 Oral] accepted paper: Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systems.

Clean, documented implementations of PPO-based algorithms for cooperative multi-agent reinforcement learning, focusing on SMAC environments. Features MLP and RNN-based MAPPO and HAPPO with various techniques.