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.

翻译：认知建模与强化学习（RL）研究中的一个问题是，研究人员花费过多时间来为实验搭建合适的计算框架。尽管当前存在许多RL算法的开源实现，但缺乏一套能够整合不同机器人模拟器与平台、数据可视化、超参数优化以及基线实验的模块化工具套件。为解决这一问题，我们提出了Scilab-RL——一个用于机器人智能体认知建模与强化学习高效研究的软件框架。该框架聚焦于基于Stable Baselines 3和OpenAI gym接口的目标条件化强化学习，原生支持实验可视化与超参数优化功能。我们描述了这些特性如何使研究人员能够以极低的时间成本开展实验，从而最大化研究产出。