mlsanityData to 14 Oct 2026

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

arXiv 2609.19074 ↗First posted 16 Sep 2026cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 39 references that are in the map. First posted on arXiv 16 Sep 2026.

Builds on

13 of 39 references.

References · 13

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2015Trust Region Policy Optimizationcited by 2,580
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  6. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  7. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  8. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  9. 2015Prioritized Experience Replaycited by 1,099
  10. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  11. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  12. 2020MushroomRL: Simplifying Reinforcement Learning Researchcited by 50
1 more
  1. 2026A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospectscited by 1

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  1. 2024Introduction to Reinforcement Learning
  2. 2025RSL-RL: A Learning Library for Robotics Research
  3. 2026A Reference Architecture of Reinforcement Learning Frameworks
  4. 2025One Framework to Rule Them All: Unifying RL-Based and RL-Free Methods in RLHF
  5. 2025Reinforcement Learning from Human Feedback
  6. 2024Syllabus: Portable Curricula for Reinforcement Learning Agents
  7. 2024A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
  8. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning