RL-suite Overview

Welcome to the RL-suite documentation! This suite is designed to provide comprehensive resources for developing and testing reinforcement learning algorithms with a focus on space robotics. Below you will find links to various sections of our documentation, including installation instructions, examples, task details, development guides, and benchmarks.

Table of Contents

• Installing the suite: Step-by-step guide on how to get the RL-suite up and running on your system.
• Examples of using the suite: A collection of examples showcasing the capabilities of the RL-suite and how to use it for your projects.
• Task Details: Detailed descriptions of the tasks available within the suite, including their objectives, input/output specifications, and evaluation metrics.
• Developing new tasks and adding assets: Guidelines on how to extend the RL-suite by developing new tasks or adding new assets.
• Benchmarks: Benchmark results for different reinforcement learning algorithms using the tasks provided in the suite.

Getting Help

If you encounter any issues or have questions regarding the RL-suite, please don't hesitate to reach out by emailing me at abmoRobotics@gmail.com.

Thank you for exploring the RL-suite.