摘要翻译：
机器学习，特别是在未知概率环境中学习的关键方法是新的表示和计算机制。本文将量子理论与强化学习(RL)相结合，提出了一种新的量子强化学习(QRL)方法。受状态叠加原理和量子并行性的启发，介绍了一种值更新算法的框架。传统RL中的状态（动作）在QRL中被识别为本征状态（本征动作）。态（作用）集可以用量子叠加态表示，本征态（本征作用）可以根据量子测量的坍缩公设通过随机观察模拟的量子态得到。本征动作的概率由概率幅度决定，概率幅度根据奖励并行更新。分析了QRL算法的收敛性、最优性以及探索与开发之间的平衡性等相关特性，表明该算法利用概率幅值很好地折衷了探索与开发之间的关系，并通过量子并行性加快了学习速度。为了评价QRL算法的性能和实用性，给出了几个仿真实验，结果表明了QRL算法对一些复杂问题的有效性和优越性。本文的工作也是量子计算在人工智能中应用的一次有效探索。
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英文标题：
《Quantum reinforcement learning》
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作者：
Daoyi Dong, Chunlin Chen, Hanxiong Li and Tzyh-Jong Tarn
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最新提交年份：
2008
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分类信息：

一级分类：Physics        物理学
二级分类：Quantum Physics        量子物理学
分类描述：Description coming soon
描述即将到来
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  The key approaches for machine learning, especially learning in unknown probabilistic environments are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of value updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is parallelly updated according to rewards. Some related characteristics of QRL such as convergence, optimality and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speed up learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given and the results demonstrate the effectiveness and superiority of QRL algorithm for some complex problems. The present work is also an effective exploration on the application of quantum computation to artificial intelligence.
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PDF链接：
https://arxiv.org/pdf/0810.3828