  One problem with researching cognitive modeling and reinforcement learning
(RL) is that researchers spend too much time on setting up an appropriate
computational framework for their experiments. Many open source implementations
of current RL algorithms exist, but there is a lack of a modular suite of tools
combining different robotic simulators and platforms, data visualization,
hyperparameter optimization, and baseline experiments. To address this problem,
we present Scilab-RL, a software framework for efficient research in cognitive
modeling and reinforcement learning for robotic agents. The framework focuses
on goal-conditioned reinforcement learning using Stable Baselines 3 and the
OpenAI gym interface. It enables native possibilities for experiment
visualizations and hyperparameter optimization. We describe how these features
enable researchers to conduct experiments with minimal time effort, thus
maximizing research output.
