MAgent is a dynamic research platform designed specifically for many-agent reinforcement learning. Unlike traditional platforms focusing on single or few-agent scenarios, MAgent allows experimentation with hundreds to millions of agents. Today, we’ll guide you through the installation and setup process, as well as provide some troubleshooting tips along the way. Let’s get started!

Installation on Linux

To get MAgent up and running on a Linux environment, follow these steps:

• Open your terminal and run the following commands:

git clone git@github.com: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

Installation on OSX

On OSX, the installation is slightly different. Follow these instructions:

• In your terminal, type:

git clone git@github.com:geek-ai/MAgent.git
cd MAgent
brew install cmake llvm boost@1.55
brew install jsoncpp argp-standalone
brew tap david-i-cracked/homebrew-websocketpp
brew install --HEAD david-i-cracked/websocketpp
brew link --force boost@1.55
bash build.sh
export PYTHONPATH=$(pwd)/python:$PYTHONPATH

Note: There may be issues with homebrew for installing websocketpp. Please check the related issue on GitHub here.

Getting Started with MAgent

Once installed, you can dive into various examples to see MAgent in action. These examples generally take about one day to train with a GTX1080-Ti card.

If you encounter out-of-memory errors, consider reducing the infer_batch_size in your models.

Running Examples

All commands below should be executed in the root directory of the MAgent repository.

• Train the Pursuit Example:

python examples/train_pursuit.py --train

• Train the Gathering Example:

python examples/train_gather.py --train

• Train the Battle Example:

python examples/train_battle.py --train

• Play an Interactive Battle Game:

python examples/show_battle_game.py

Understanding the Code

MAgent operates in a manner similar to a vast city where each agent is like an individual citizen. The collective behavior of these citizens leads to larger outcomes—much like how multiple agents in MAgent can work together to solve complex tasks.

Just as citizens can organize themselves into groups or teams to achieve a goal (like city cleanup), agents in MAgent can share knowledge and resources, communicate with one another, and optimize strategies to overcome obstacles or complete assigned tasks.

Troubleshooting

If you run into any issues during installation or operation, consider the following tips:

• Ensure all dependencies are installed appropriately. For Linux users, check the installation of cmake, libboost, and other necessary libraries.
• On OSX, ensure that you’ve successfully linked the websocketpp library.
• If you experience performance issues or memory errors, try reducing the batch size or check your GPU memory allocation.
• For updates on potential issues and improvements, visit the [MAgent GitHub repository](https://github.com/Farama-Foundation/MAgent).

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.