MLPro - The Integrative Middleware Framework for Standardized Machine Learning in Python

MLPro provides complete, standardized, and reusable functionalities to support your scientific research, educational tasks or industrial projects in machine learning.

Key Features

a) Open, modular and extensible architecture

• Overarching software infrastructure (mathematics, data management and plotting, UI framework, logging, ...)
• Fundamental ML classes for adaptive models and their training and hyperparameter tuning

b) MLPro-RL: Sub-Package for Reinforcement Learning

• Powerful Environment templates for simulation, training and real operation
• Templates for single-agents, model-based agents (MBRL) with action planning to multi-agents (MARL)
• Advanced training/tuning funktionalities with separate evaluation and progress detection
• Growing pool of reuseable environments of automation and robotics

c) MLPro-GT: Sub-Package for Native Game Theory and Dynamic Games

• Templates for native game theory regardless number of players and type of games
• Templates for multi-players in dynamic games, including game boards, players, and many more
• Reuse of advanced training/tuning classes and multi-agent environments of sub-package MLPro-RL

d) Numerous executable self study examples

e) Integration of established 3rd party packages

MLPro provides wrapper classes for:

• Environments of OpenAI Gym and PettingZoo
• Policy Algorithms of Stable Baselines 3
• Hyperparameter tuning with Hyperopt

Documentation

The Documentation is available here: https://mlpro.readthedocs.io/

Development

• Consequent object-oriented design and programming (OOD/OOP)
• Quality assurance by test-driven development
• Hosted and managed on GitHub
• Agile CI/CD approach with automated test and deployment
• Clean code paradigma

Project and Team

Project MLPro was started in 2021 by the Group for Automation Technology and Learning Systems at the South Westphalia University of Applied Sciences, Germany.

MLPro is designed and developed by Detlef Arend, Steve Yuwono, M Rizky Diprasetya, and further contributors.

How to contribute

If you want to contribute, please read CONTRIBUTING.md