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The common purpose of applying reinforcement learning (RL) to assetmanagement is the maximization of profit. The extrinsic reward function used tolearn an optimal strategy typically does not take into account any otherpreferences or constraints. We have developed a regularization method thatensures that strategies have global intrinsic affinities, i.e., differentpersonalities may have preferences for certain assets which may change overtime. We capitalize on these intrinsic policy affinities to make our RL modelinherently interpretable. We demonstrate how RL agents can be trained toorchestrate such individual policies for particular personality profiles andstill achieve high returns.

Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management