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The proliferation of artificial intelligence is increasingly dependent onmodel understanding. Understanding demands both an interpretation - a humanreasoning about a model's behavior - and an explanation - a symbolicrepresentation of the functioning of the model. Notwithstanding the imperativeof transparency for safety, trust, and acceptance, the opacity ofstate-of-the-art reinforcement learning algorithms conceals the rudiments oftheir learned strategies. We have developed a policy regularization method thatasserts the global intrinsic affinities of learned strategies. These affinitiesprovide a means of reasoning about a policy's behavior, thus making itinherently interpretable. We have demonstrated our method in personalizedprosperity management where individuals' spending behavior in time dictatetheir investment strategies, i.e. distinct spending personalities may havedissimilar associations with different investment classes. We now explain ourmodel by reproducing the underlying prototypical policies with discretizedMarkov models. These global surrogates are symbolic representations of theprototypical policies.

Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models