MLPro 1.0 - Standardized reinforcement learning and game theory in Python

Abstract

Nowadays there are numerous powerful software packages available for most areas of machine learning (ML). These can be roughly divided into frameworks that solve detailed aspects of ML and those that pursue holistic approaches for one or two learning paradigms. For the implementation of own ML applications, several packages often have to be involved and integrated through individual coding. The latter aspect in particular makes it difficult for newcomers to get started. It also makes a comparison with other works difficult, if not impossible. Especially in the area of reinforcement learning (RL), there is a lack of frameworks that fully implement the current concepts up to multi-agents (MARL) and model-based agents (MBRL). For the related field of game theory (GT), there are hardly any packages available that aim to solve real-world applications. Here we would like to make a contribution and propose the new framework MLPro, which is designed for the holistic realization of hybrid ML applications across all learning paradigms. This is made possible by an additional base layer in which the fundamentals of ML (interaction, adaptation, training, hyperparameter optimization) are defined on an abstract level. In contrast, concrete learning paradigms are implemented in higher sub-frameworks that build on the conventions of this additional base layer. This ensures a high degree of standardization and functional recombinability. Proven concepts and algorithms of existing frameworks can still be used. The first version of MLPro includes sub-frameworks for RL and cooperative GT.

Keywords

• Machine learning
• Reinforcement learning
• Game theory
• Automation
• Scientific software development
• Python