两篇最新的arXiv预印本探讨了强化学习（RL）与量子计算的交叉领域。第一篇论文为经典和量子RL提供了一个入门教程，重点关注面向本科生的实际编码应用。第二篇论文在生成模型下引入了新颖的经典和量子在线强化学习算法，证明量子算法可以通过打破典型的O(sqrt(T))障碍，实现比经典算法更好的遗憾界限。 AI
影响 这些论文有助于加深对强化学习与量子计算交叉领域的理论理解和算法发展。
排序理由 两篇在arXiv上发表的学术论文，讨论强化学习和量子算法。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2601.08662v3 Announce Type: replace Abstract: 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 applic…
arXiv:2507.22854v3 Announce Type: replace-cross Abstract: We propose novel classical and quantum online algorithms for learning finite- and infinite-horizon Markov Decision Processes (MDPs). Our algorithms are based on a hybrid online-offline reinforcement learning model wherein …