Reinforcement Learning with Intrinsic Affinity for Personalized Asset Management

• URL: http://arxiv.org/abs/2204.09218v1
• Date: Wed, 20 Apr 2022 04:33:32 GMT
• Title: Reinforcement Learning with Intrinsic Affinity for Personalized Asset Management
• Authors: Charl Maree and Christian W. Omlin
• Abstract summary: We develop a regularization method that ensures that strategies have global intrinsic affinities. We capitalize on these intrinsic affinities to make our 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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