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...Developed by SAIL at Singapore, it leverages C++ backend and Python frontend for extremely high-speed environment interaction, supporting thousands of environments running in parallel on a single machine. It's compatible with Gymnasium API and RLlib, making it suitable for scalable training pipelines.
The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners.
Obtaining the teachingbox:
FOR USERS:
If you want to download the latest releases, please visit:
http://search.maven.org/#search|ga|1|teachingbox
FOR DEVELOPERS:
1) If you use Apache Maven, just add the following dependency to your pom.xml:
<dependency>
<groupId>org.sf.teachingbox</groupId>
<artifactId>teachingbox-core</artifactId>
<version>1.2.3</version>
</dependency>
2) If you want to check out the most recent source-code:
git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core
Documentation:
https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/
This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.