RLLBC-Lib: An Educational Code Library for Reinforcement Learning and Learning-Based Control

2026-09-16

AI Summary

为降低强化学习入门难度，开发了RLLBC-Lib代码库，通过实现表格型和深度RL方法来阐明理论基础，并支持自动评分的编程作业。

Abstract

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.

Problem

• Reinforcement Learning
• Educational Tools
• Learning-Based Control

Innovation

• Reinforcement Learning
• Educational Code Library
• Learning-Based Control
• Tabular RL
• Deep RL