on-policy
★ 2.1k·MIT·Updated 2024-07-18
This repository implements MAPPO, a multi-agent variant of PPO, widely used in cooperative multi-agent games and research. It provides robust implementations for various multi-agent environments like StarCraft II, Hanabi, and Google Research Football, along with detailed training scripts and hyperparameter guidance.
#Multi-Agent Reinforcement Learning#PPO#MAPPO#Reinforcement Learning#PyTorch
01
Features
01Implementation of MAPPO (Multi-Agent PPO)
02Support for diverse multi-agent environments (e.g., StarCraft II, Hanabi)
03Ready-to-use training scripts for various scenarios
04Detailed hyperparameter guidance and updated results
05Default support for shared policy among agents
02
Compatibility
StarCraftII (SMAC)
Native
Verified via docs
Hanabi
Native
Verified via docs
Multiagent Particle-World Environments (MPEs)
Native
Verified via docs
Google Research Football (GRF)
Native
Verified via docs
StarCraftII (SMAC) v2
Native
Verified via docs
03
Quick start
1
$ pip install -e .
04
Use cases
↳Research and experimentation in cooperative multi-agent reinforcement learning
↳Benchmarking and evaluating PPO's effectiveness in MARL scenarios
↳Training AI agents for popular multi-agent games like StarCraft II and Hanabi
05
Alternatives
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