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Host LLMs in Production With On-Demand GPUs
NVIDIA L4 GPUs. 5-second cold starts. Scale to zero when idle.
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The Teachingbox uses advanced machinelearning 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/
Using reinforcement learning with relative input to train Ms. Pac-Man
This Java-application contains all required components to simulate a game of Ms. Pac-Man and let an agent learn intelligent playing behaviour using reinforcement learning and either Q-Learning or SARSA.
The framework was developed by Luuk Bom and Ruud Henken, under supervision of Marco Wiering, Department of Artificial Intelligence, University of Groningen. It formed the basis of a bachelor's thesis titled "Using reinforcement learning with relative input to train Ms. Pac-Man", L.A.M. Bom (2012).