英文标题：
《Dreaming machine learning: Lipschitz extensions for reinforcement
  learning on financial markets》
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作者：
J.M. Calabuig, H. Falciani and E.A. S\\\'anchez-P\\\'erez
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最新提交年份：
2020
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英文摘要：
  We consider a quasi-metric topological structure for the construction of a new reinforcement learning model in the framework of financial markets. It is based on a Lipschitz type extension of reward functions defined in metric spaces. Specifically, the McShane and Whitney extensions are considered for a reward function which is defined by the total evaluation of the benefits produced by the investment decision at a given time. We define the metric as a linear combination of a Euclidean distance and an angular metric component. All information about the evolution of the system from the beginning of the time interval is used to support the extension of the reward function, but in addition this data set is enriched by adding some artificially produced states. Thus, the main novelty of our method is the way we produce more states -- which we call \"dreams\" -- to enrich learning. Using some known states of the dynamical system that represents the evolution of the financial market, we use our technique to simulate new states by interpolating real states and introducing some random variables. These new states are used to feed a learning algorithm designed to improve the investment strategy by following a typical reinforcement learning scheme.
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中文摘要：
在金融市场的框架下，我们考虑一种准度量拓扑结构来构建一个新的强化学习模型。它基于度量空间中定义的奖励函数的Lipschitz型扩展。具体而言，McShane和Whitney扩展被视为一个奖励函数，该函数由对给定时间投资决策产生的效益的总体评估来定义。我们将度量定义为欧氏距离和角度度量分量的线性组合。从时间间隔开始，有关系统演化的所有信息都用于支持奖励函数的扩展，但此外，通过添加一些人工生成的状态，该数据集也得到了丰富。因此，我们的方法的主要创新之处在于我们产生更多状态的方式——我们称之为“梦”——以丰富学习。利用代表金融市场演化的动力系统的一些已知状态，我们使用我们的技术通过插值真实状态和引入一些随机变量来模拟新状态。这些新状态被用来提供一种学习算法，该算法通过遵循典型的强化学习方案来改进投资策略。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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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一级分类：Mathematics        数学
二级分类：Functional Analysis        功能分析
分类描述：Banach spaces, function spaces, real functions, integral transforms, theory of distributions, measure theory
Banach空间，函数空间，实函数，积分变换，分布理论，测度理论
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