论文标题
强化学习具有对个性化繁荣管理的内在亲和力
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management
论文作者
论文摘要
将强化学习（RL）应用于资产管理的共同目的是利润的最大化。用于学习最佳策略的外部奖励功能通常不会考虑任何其他偏好或约束。我们已经开发了一种正则化方法，该方法可确保策略具有全球固有亲和力，即，不同的个性可能对某些资产可能会随着时间而变化。我们利用这些内在政策亲和力，使我们的RL模型固有地解释。我们演示了如何培训RL代理，以为特定的个性概况编排此类政策，并仍然获得高回报。
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 any other preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain assets which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns.
