AgentIndex icon
AgentIndex
MAgent logo
MAgent
★ 1.8k
vs
on-policy logo
on-policy
★ 2.1k

MAgent vs on-policy

MAgent: MAgent is a research platform designed for many-agent reinforcement learning, supporting scaling from hundreds to millions of agents. It provides a robust environment for exploring artificial collective intelligence, offering compatibility with Linux and OS X and various deep learning frameworks.; on-policy: 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.

01

TL;DR

MAgent logo
MAgentLLM Infra · ★ 1.8k

Many-agent reinforcement learning research

Read MAgent full review →
on-policy logo
on-policyLLM Infra · ★ 2.1k

Research and experimentation in cooperative multi-agent reinforcement learning

Read on-policy full review →
02

Side-by-Side Comparison

Field
MAgent logoMAgent
on-policy logoon-policy
Category
LLM Infra
LLM Infra
Stars
★ 1.8k
★ 2.1k
License
MIT
MIT
Install Command
git clone [email protected]:geek-ai/MAgent.git && cd MAgent && sudo apt-get install cmake libboost-system-dev libjsoncpp-dev libwebsocketpp-dev && bash build.sh && export PYTHONPATH=$(pwd)/python:$PYTHONPATH
pip install -e .
Updated
4y ago
2y ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
Multi-Agent Reinforcement Learning, Simulation Platform, Collective Intelligence
Multi-Agent Reinforcement Learning, PPO, MAPPO
03

Features

MAgent logoMAgent
01Scalable Many-Agent Support (hundreds to millions)
02Flexible Agent Development (rule-based or deep learning)
03Diverse Demo Scenarios (pursuit, gathering, battle)
04Interactive Battle Game Mode
05Integrated Baseline RL Algorithms (DQN, DRQN, A2C)
on-policy logoon-policy
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
04

Use Cases

MAgent logoMAgent
↳Many-agent reinforcement learning research
↳Development of artificial collective intelligence
↳Benchmarking RL algorithms in large-scale environments
on-policy logoon-policy
↳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
FAQ

FAQ

What is the difference between MAgent and on-policy?
Both MAgent and on-policy are in the LLM Infra category. MAgent has 1.8k stars, while on-policy has 2.1k stars.
Which is better, MAgent or on-policy?
The best choice depends on your use case. Choose MAgent if Many-agent reinforcement learning research, and on-policy if Research and experimentation in cooperative multi-agent reinforcement learning.
Is MAgent free or open source?
Yes, MAgent is open source on GitHub (MIT).
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Cloud Infrastructure
Ad

Deploy on DigitalOcean

Host your MCP servers, Docker containers, and AI agents with $200 free credit over 60 days.

Claim $200 Free Credit
→

Related

Alternatives to MAgent →Alternatives to on-policy →MAgent details →on-policy details →