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This project features a set of tools/applications written in either C++ or C# designed to make experiments with Reinforcement Learning algorithms on control tasks with continuous state and action spaces. The main goal is to provide an easy-to-use environment in which end-users (no programming skills required) can design, run, and monitor/view experiments, and then analyze the results. The most prominent features are:
- Experiment parameters can be given a set of values to perform a parameter sweep
- All the different combinations can be run in parallel using the built-in distributed execution mode
- The results of an experiment can be analyzed with customizable plots
- The behavior of system can also be viewed live or after an experiment has finished
- It supports Windows (x86 and x64) and Linux (x64).
- End-users who want to run the binaries should read this guide.
- Developers who want to compile/adapt the sources should read this guide.
If you use our software in your research, we kindly ask you to reference 
The code features contributions from:
- Unai Tercero (Badger and Herd Agent)
- Asier Rodríguez (Bullet worlds)
- Alejandro Guerra (Badger and Herd Agent)
- Roland Zimmermann (Badger, OffPAC, INAC, Tile Coding, ... and all about CNTK and Deep RL)
Except a few fixes by others (check the commmits), the rest of the code has been written and is mantained by Borja Fernández-Gauna from the Group of Computational Intelligence at the University of the Basque Country (UPV/EHU), so any questions/suggestions can be directed to my email address: borja.fernandez'at'ehu.eus.