  In reinforcement learning (RL) research, simulations enable benchmarks
between algorithms, as well as prototyping and hyper-parameter tuning of
agents. In order to promote RL both in research and real-world applications,
frameworks are required which are on the one hand efficient in terms of running
experiments as fast as possible. On the other hand, they must be flexible
enough to allow the integration of newly developed optimization techniques,
e.g. new RL algorithms, which are continuously put forward by an active
research community. In this paper, we introduce Karolos, a RL framework
developed for robotic applications, with a particular focus on transfer
scenarios with varying robot-task combinations reflected in a modular
environment architecture. In addition, we provide implementations of
state-of-the-art RL algorithms along with common learning-facilitating
enhancements, as well as an architecture to parallelize environments across
multiple processes to significantly speed up experiments. The code is open
source and published on GitHub with the aim of promoting research of RL
applications in robotics.
