Offline Multi-Agent Reinforcement Learning (MARL) opens doors to numerous real-world applications by leveraging static datasets to create decentralized controllers. Yet, a significant challenge remains: the lack of standardized benchmarks to evaluate the progress of offline MARL research. Enter Off-the-Grid MARL (OG-MARL), designed to bridge this gap by offering a diverse collection of datasets equipped with benchmarks on popular environments. This guide will walk you through setting up and utilizing OG-MARL effectively.

Understanding OG-MARL: A Perfect Analogy

Imagine a group of chefs in a culinary school, each perfecting their unique recipes. They all need high-quality ingredients, clear instructions, and a reliable kitchen to showcase their culinary skills. OG-MARL is like a well-organized culinary school that provides top-notch ingredients (datasets) and a community of chefs (researchers), ensuring everyone learns and improves their skills in the culinary arts (offline MARL). Just as the chefs can rely on the school’s resources, you can depend on OG-MARL’s datasets and baselines for effective offline reinforcement learning.

Quickstart Guide

Here’s how to get started with OG-MARL:

• Clone the Repository:

  git clone https://github.com/instadeepai/og-marl.git

• Install OG-MARL and its Requirements:

  pip install -r requirements.txt

  pip install -e .

• Download Environment Files: (Using SMACv1 as an example)

  bash install_environments_smacv1.sh

• Train an Offline System:

  python og_marl/tf2/systems/iql_cql.py task.source=og_marl task.env=smac_v1 task.scenario=3m task.dataset=Good

Utilizing the Dataset API

To access the OG-MARL dataset API, visit the example notebook here.

Exploring Datasets and Environments

OG-MARL leverages a range of datasets, categorized by quality: Good, Medium, Poor, and Replay. This allows for testing on various qualities and scenarios:

• SMAC v1 with multiple scenarios such as 3m or 8m.
• Flatland for multi-agent challenges with trains.
• MAMuJoCo environments for continuous action space.

Troubleshooting

If you encounter issues during setup, try the following:

• Ensure you are using Python (preferably 3.10) and Ubuntu 20.04 for compatibility.
• Check that you have installed all required packages as indicated in the requirements.txt file.
• If you experience difficulties downloading datasets from Hugging Face, ensure you have a stable internet connection.

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Conclusion

With OG-MARL, researchers can now benchmark their work against reliable datasets, fostering significant advancements in offline multi-agent reinforcement learning.

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.