Maze: Applied Reinforcement Learning with Python

Maze is an application oriented Reinforcement Learning framework with the vision to:
  • Enable AI-based optimization for a wide range of industrial decision processes.
  • Make RL as a technology accessible to industry and developers.
Our ultimate goal is to cover the complete development life cycle of RL applications ranging from simulation engineering up to agent development, training and deployment.
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Getting Started

You can also find an extensive overview of Maze in the table of contents as well as the API documentation.

Spotlights

Below we list of some of Maze’s key features. The list is far from exhaustive but none the less a nice starting point to dive into the framework.

Warning

This is a preliminary, non-stable release of Maze. It is not yet complete and not all of our interfaces have settled yet. Hence, there might be some breaking changes on our way towards the first stable release.

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Documentation Overview

Below you find an overview of the general Maze framework documentation, which is beyond the API documentation. The listed pages motivate and explain the underlying concepts but most importantly also provide code snippets and minimum working examples to quickly get you started.

Workflow

Policy and Value Networks

Training

Concepts and Structure

Environment Customization

Best Practices and Tutorials

Logging and Monitoring

Scaling the Training Process

Indices and tables