  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.
