aiqbench analysis
An open-source teaching library that builds reinforcement learning methods from scratch, from tabular temporal-difference learning to deep policy gradients, with a shared interface for comparing them. Its main value is pedagogical: each update rule is short enough to read and modify, which suits courses that assess students' understanding of the maths rather than just working code. Readers interested in learning RL fundamentals, or in how deep RL updates connect to temporal-difference error, will find it useful, though it is a teaching tool rather than a source of new results.
rl_rob: A transparent, from-scratch Python library for teaching reinforcement learning from tabular methods to deep policy gradients
arXiv:2610.41303v1 Announce Type: new Abstract: rl_rob is an open-source Python library for teaching reinforcement learning. It implements the arc of a modern course, from tabular temporal-difference learning through linear function approximation to deep value-based and policy-gradient methods, with every algorithm written from first principles so that its update rule is visible in a few lines of code. All agents share one interface, so learners can compare methods directly across stages and trace a deep reinforcement learning update from the temporal-difference error to the weight change. The library supports teaching and assessment in which students implement, modify and explain update rules, rather than only produce working code.
Abstract:rl_rob is an open-source Python library for teaching reinforcement learning. It implements the arc of a modern course, from tabular temporal-difference learning through linear function approximation to deep value-based and policy-gradient methods, with every algorithm written from first principles so that its update rule is visible in a few lines of code. All agents share one interface, so learners can compare methods directly across stages and trace a deep reinforcement learning update from the temporal-difference error to the weight change. The library supports teaching and assessment in which students implement, modify and explain update rules, rather than only produce working code.
Machine Learning (cs.LG)
arXiv:2610.41303 [cs.LG]
(or arXiv:2610.41303v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.41303 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 195 words
Related coverage
Teachy Mini: Development and Preliminary Evaluation of a Knowledge-Based Generative Social Robot for Higher Education
arXiv · Jul 28, 2026
SR4-Fit: A Unified Interpretable Rule-Based Machine Learning Framework for Informative and Trustworthy Decision-Making
arXiv · Sep 30, 2026
Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation
arXiv · Jul 23, 2026
Compiling VGDL into Causal Models
arXiv · Sep 9, 2026