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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/
Features
- Reinforcement Learning
- RL-Environments: nArmedBandit, WindyGridWorld, MountainCar, PoleBalance, PoleSwingUp, CartPole, RL-Glue, DynamicalSystems, Grid-Worlds such as Cliff-Walking (including an Grid-World Editor), Crawling Robot 3D Simulation (PythonGL), etc.
- RL-Learner: RBF, Tabular, TileCoding, Neural-Fitted Q-Iteration
- RL-Policies: Greedy, EpsilonGreedy, Softmax, VDBE / VDBE-Softmax, REINFORCE
- Visualizations: Mountain-Car, PoleBalance, Crawler 3D
Project Samples
