发表机构
Institute for Data Science in Mechanical Engineering (DSME); RWTH Aachen University（机械工程数据科学研究所; 亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RLLBC-Lib 是一个教育代码库，通过表格型与深度 RL 实现降低学习门槛，并支持自动评分作业。
AI 中文摘要
强化学习（RL）是一个令人兴奋的概念，也是一个值得分享的显著成功故事。然而，RL 建立在不同对象之间相当复杂的交互之上，这些交互在多个周期中展开。这种动态通常最好通过易于访问的实现来解释。我们提出了 RLLBC-Lib，一个精心设计的代码库，旨在降低学生和其他学习者在基于学习的控制背景下学习 RL 的入门门槛。RLLBC-Lib 的核心包含一个全面的表格型 RL 方法库，以加强对理论基础的理解。一个深度 RL 库遵循相同的设计原则，强调了简单表格型与最先进的深度 RL 方法之间的相似性。此外，RLLBC-Lib 提供了一系列实现，展示了核心 RL 原理，并将 RL 与其他基于学习的控制方法进行对比。最后，RLLBC-Lib 为创建具有自动评分的编程作业提供了理想的基础。
英文摘要
Reinforcement learning (RL) is an exciting concept as well as a remarkable success story worth sharing. However, RL builds on rather complex interactions between different objects that play out over several cycles. Such dynamics are often best explained with an easily accessible implementation. We present RLLBC-Lib, a carefully crafted code library with the goal of lowering the entry barrier for students and other learners of RL in the context of learning-based control. At its heart, RLLBC-Lib comprises a comprehensive library of tabular RL approaches to enforce a clear understanding of the theoretical foundations. A deep RL library follows the same design principles, underscoring the parallels between simple tabular and state-of-the-art deep RL approaches. Additionally, RLLBC-Lib provides a collection of implementations illustrating core RL principles and contrasting RL to other learning-based control approaches. Finally, RLLBC-Lib provides an ideal basis for creating programming assignments with automated grading.
