Deep Reinforcement Learning (DRL) is increasingly used to
train robots to perform complex and delicate tasks, while the develop-
ment of realistic simulators contributes to the acceleration of research on
DRL for robotics. However, it is still not straightforward to employ such
simulators in the typical DRL pipeline, since their steep learning curve
and the enormous amount of development required to interface with DRL
methods significantly restrict their use by researchers. To overcome these
limitations, in this work we present an open-source framework that com-
bines an established interface used by DRL researchers, the OpenAI Gym
interface, with the state-of-the-art Webots robot simulator in order to
provide a standardized way to employ DRL in various robotics scenarios.
Deepbots aims to enable researchers to easily develop DRL methods in
Webots by handling all the low-level details and reducing the required de-
velopment effort.

Deepbots: A Webots-based Deep Reinforcement Learning Framework for Robotics

technical paper

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Nikolaos Passalis

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Deepbots: A Webots-based Deep Reinforcement Learning Framework for Robotics

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