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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.

Important note: We are currently not offering any release packages! Instead, please checkout the SVN trunk for obtainig the latest version of the Teachingbox:

svn checkout https://svn.code.sf.net/p/teachingbox/code/trunk teachingbox-trunk

or browse files: https://sourceforge.net/p/teachingbox/code/HEAD/tree/trunk/

Teachingbox Web Site


  • 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

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Information Technology, Science/Research, Developers

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