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Apr, 2022
利用内在亲和力进行个性化繁荣管理的强化学习
Reinforcement Learning with Intrinsic Affinity for Personalized Asset Management
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Charl Maree, Christian W. Omlin
TL;DR
本研究开发了一种正则化方法，以确保策略具有全局内在亲和力，并利用这些内在策略亲和力使我们的强化学习模型具有内在可解释性。 我们展示了如何训练RL agents来编排特定个性类型的个体策略，并仍然获得高回报。
Abstract
The common purpose of applying
reinforcement learning
(RL) to
asset management
is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account a
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