MALib is an innovative parallel framework designed specifically for population-based reinforcement learning. It integrates sophisticated methods such as Policy Space Response Oracle, Self-Play, and Neural Fictitious Self-Play, offering a higher-level abstraction that promotes code reuse and flexibility across various distributed computing paradigms.

In this guide, we will walk you through the steps to install MALib, set it up with various environments, and run quick-start examples.

Installation

Getting MALib up and running is a breeze, especially if you are using Python versions 3.8 and above. Below, we’ll detail the installation process, primarily based on Ubuntu 18.04 and above.

bash
# Create a conda environment for MALib
conda create -n malib python==3.8 -y
conda activate malib
# Install dependencies
./install.sh

Supported Environments

MALib is versatile and integrates with several renowned reinforcement learning environments. Here is a quick list of some of the supported environments:

• OpenSpiel: A framework for Reinforcement Learning in games.
• Gym: An open source environment collection for developing reinforcement learning algorithms.
• Google Research Football: RL environment based on the open-source game, Gameplay Football.
• SMAC: A research environment for collaborative MARL based on StarCraft II.
• PettingZoo: A library for multi-agent reinforcement learning.
• DexterousHands: An environment collection for bimanual dexterous manipulation tasks.

For further customization, explore MALib’s environment interfaces within the framework.

Algorithms and Scenarios

MALib boasts numerous algorithms tailored for population-based reinforcement learning. Some of the scenarios include:

• Single-stream PSRO scenario: Combines single-stream population-based reinforcement learning with empirical game theoretical analysis.
• Multi-stream PSRO scenario: Follows a similar approach but for multi-stream algorithms.
• Multi-agent Reinforcement Learning scenario: Supports multiple single-agent reinforcement learning through distributed techniques.

You can find the list of algorithms on the algorithms table.

Quick Start

To kick off your first experiments with MALib, set the Python path as follows:

bash
cd malib
export PYTHONPATH=.
# To run PSRO example for Kuhn Poker game
python examples/run_psro.py
# To run RL example for CartPole-v1 game
python examples/run_gym.py

Documentation

Access the online documentation at MALib Docs. If you prefer a local version, compile it by following these steps:

bash
pip install -e.[dev]
make docs-compile
# Start a web server to view the documentation
make docs-view

Troubleshooting

If you encounter issues during the installation or execution of MALib, consider the following troubleshooting tips:

• Ensure that all dependencies are correctly installed and compatible with your Python version.
• Check that your conda environment is activated when running scripts.
• Look into the logs for any error messages that might provide clues to the issue.

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Conclusion

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.