  The increased complexity of state-of-the-art reinforcement learning (RL)
algorithms have resulted in an opacity that inhibits explainability and
understanding. This has led to the development of several post-hoc
explainability methods that aim to extract information from learned policies
thus aiding explainability. These methods rely on empirical observations of the
policy and thus aim to generalize a characterization of agents' behaviour. In
this study, we have instead developed a method to imbue a characteristic
behaviour into agents' policies through regularization of their objective
functions. Our method guides the agents' behaviour during learning which
results in an intrinsic characterization; it connects the learning process with
model explanation. We provide a formal argument and empirical evidence for the
viability of our method. In future work, we intend to employ it to develop
agents that optimize individual financial customers' investment portfolios
based on their spending personalities.
