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Many Reinforcement Learning algorithms assume a Markov reward function toguarantee optimality. However, not all reward functions are known to be Markov.In this paper, we propose a framework for mapping non-Markov reward functionsinto equivalent Markov ones by learning a Reward Machine - a specialized rewardautomaton. Unlike the general practice of learning Reward Machines, we do notrequire a set of high-level propositional symbols from which to learn. Rather,we learn \emph{hidden triggers} directly from data that encode them. Wedemonstrate the importance of learning Reward Machines versus theirDeterministic Finite-State Automata counterparts, for this task, given theirability to model reward dependencies in a single automaton. We formalize thisdistinction in our learning objective. Our mapping process is constructed as anInteger Linear Programming problem. We prove that our mappings provideconsistent expectations for the underlying process. We empirically validate ourapproach by learning black-box non-Markov Reward functions in the OfficeworldDomain. Additionally, we demonstrate the effectiveness of learning dependenciesbetween rewards in a new domain, Breakfastworld.

Detecting Hidden Triggers: Mapping Non-Markov Reward Functions to Markov