This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations. It focuses on bridging the gap between RL theory and practical coding applications, addressing common challenges that students face when transitioning from conceptual understanding to implementation. Through hands-on examples and approachable explanations, the tutorial aims to equip students with the foundational skills needed to confidently apply RL techniques in real-world scenarios.
@article{arxiv.2601.08662,
title = {From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial},
author = {Abhijit Sen and Sonali Panda and Mahima Arya and Subhajit Patra and Zizhan Zheng and Denys I. Bondar},
journal= {arXiv preprint arXiv:2601.08662},
year = {2026}
}