BenchMARL, or Benchmarking Multi-Agent Reinforcement Learning, is an innovative library tailored for those diving into the realm of multi-agent systems. Designed for reproducibility and benchmarking across various algorithms and environments, BenchMARL creates an effortless experience for researchers and developers alike. This guide will walk you through how to get started with BenchMARL and troubleshoot any issues you may encounter along the way.

Table of Contents

• How to Use
• Concept
• Features
• Troubleshooting

How to Use BenchMARL

Notebooks

BenchMARL provides interactive notebooks to help you run experiments smoothly. You can open them easily using the link below:

Open In Colab

Installation

First, ensure you have the necessary libraries installed. Below are the steps for installing BenchMARL and its dependencies:

Install TorchRL

You can install TorchRL via PyPI—just type:

pip install torchrl

For more details or installing nightly versions, you can check the TorchRL installation guide.

Install BenchMARL

Next, install BenchMARL either from GitHub or clone it locally:

pip install benchmarl

git clone https://github.com/facebookresearch/BenchMARL.git
pip install -e BenchMARL

Install Environments

You can install specific environment dependencies separately:

pip install vmas
pip install pettingzoo[all]
pip install dm-meltingpot
# For SMACv2, follow the environment repo instructions

Running Experiments

Your experiments can be launched with a simple command from the command line:

python benchmarl/run.py algorithm=mappo task=vmas_balance

To run multiple configurations using Hydra, you can execute:

python benchmarl/run.py -m algorithm=mappo,qmix,masac task=vmas_balance,vmas_sampling seed=0,1

Concept

The essence of BenchMARL is built around unifying different multi-agent environments and algorithms. Imagine you are an artist with multiple canvases, each representing an environment or an algorithm. Instead of paintbrushes that are uniquely suited to each canvas, BenchMARL provides a common set of brushes that are adaptable enough to paint any canvas beautifully. This allows you to compare different algorithms with ease, and reproducibility remains at the core of the design.

Features

BenchMARL is packed with features that elevate the user experience:

• Test CI with integration and training routines
• Seamless integration in the TorchRL ecosystem
• Powerful logging capabilities
• Checkpointing for better experiment management
• Customizable callbacks for monitoring training

Troubleshooting

While using BenchMARL, you might encounter some common issues. Here are some troubleshooting tips:

• If installations fail, ensure that your Python environment is properly set up and that the latest version of pip is being used.
• For command line errors, double-check your syntax and ensure all paths are correct.
• If your experiments are running too slow, consider checking your machine’s resources as they may be maxed out, or try running fewer simultaneous tasks.
• Lastly, if you need further assistance, community support is available via Discord and GitHub issues.

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.