Deepbots framework docs - Reinforcement Learning in Webots
Note
The documentation site is under active development. Check out this PR for an idea on what to expect in the future!
Deepbots is a simple framework which is used as “middleware” between the free and open-source Cyberbotics’ Webots robot simulator and Reinforcement Learning (RL) algorithms. When it comes to RL, gym environments have been established as the most used interface between the actual application and the RL algorithm.
Deepbots is a framework which follows the gym interface logic and bridges the gap between the gym environment and the simulator to enable you to easily create custom RL environments in Webots.
Contents
General
Official resources
On the deepbots-tutorials repository you can find the official tutorials for deepbots
On the deepworlds repository you can find examples of deepbots being used. <br>Feel free to contribute your own!
Citation
Conference paper (AIAI2020): https://link.springer.com/chapter/10.1007/978-3-030-49186-4_6
@InProceedings{10.1007/978-3-030-49186-4_6,
author="Kirtas, M.
and Tsampazis, K.
and Passalis, N.
and Tefas, A.",
title="Deepbots: A Webots-Based Deep Reinforcement Learning Framework for Robotics",
booktitle="Artificial Intelligence Applications and Innovations",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="64--75",
isbn="978-3-030-49186-4"
}
Acknowledgments
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.