A $Q$ -Learning Approach to Derive Optimal Consumption and Investment Strategies

Weissensteiner A.

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A $Q$ -Learning Approach to Derive Optimal Consumption and Investment Strategies

机译：一种$ Q $-得出最佳消费和投资策略的学习方法

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In this paper, we consider optimal consumption and strategic asset allocation decisions of an investor with a finite planning horizon. A Q-learning approach is used to maximize the expected utility of consumption. The first part of the paper presents conceptually the implementation of Q -learning in a discrete state-action space and illustrates the relation of the technique to the dynamic programming method for a simplified setting. In the second part of the paper, different generalization methods are explored and, compared to other implementations using neural networks, a combination with self-organizing maps (SOMs) is proposed. The resulting policy is compared to alternative strategies.

机译：在本文中，我们考虑了具有有限计划范围的投资者的最佳消费和战略资产分配决策。 Q学习方法用于最大化预期的消费效用。本文的第一部分从概念上介绍了离散状态作用空间中Q学习的实现，并说明了该技术与简化设置的动态编程方法之间的关系。在本文的第二部分中，探索了不同的泛化方法，并且与使用神经网络的其他实现相比，提出了与自组织映射（SOM）的组合。将由此产生的策略与替代策略进行比较。

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来源
《Neural Networks, IEEE Transactions on》 |2009年第8期|p.1234-1243|共10页
作者
Weissensteiner A.;
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作者单位

Dept. of Banking & Finance, Univ. of Innsbruck, Innsbruck, Austria;

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正文语种 eng
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关键词
dynamic programming; finance; learning (artificial intelligence); self-organising feature maps; Q-learning approach; asset allocation decision strategy; dynamic programming method; investment strategies; neural network; optimal consumption; self-organizing map; $Q$-learning; Asset allocation; reinforcement learning; self-organizing maps (SOMs);

机译：动态规划;财务;学习（人工智能）;自组织特征图;Q学习方法;资产分配决策策略;动态规划方法;投资策略;神经网络;最优消费量;自组织图;$ Q $学习;资产分配;强化学习;自组织图（SOM）;

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机译：允许消费约束，破产和福利的最优消费和投资政策

A $Q$ -Learning Approach to Derive Optimal Consumption and Investment Strategies

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