Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management
This work addresses personalized asset management for investors by enabling interpretable RL strategies, though it appears incremental as it builds on existing RL methods with a new regularization approach.
The paper tackles the problem of incorporating personal preferences into reinforcement learning for asset management by introducing a regularization method that ensures strategies have global intrinsic affinities, resulting in RL agents achieving high returns while being interpretable for individual personality profiles.
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.